Data processing method and apparatus based on edge computing, device, and storage medium

ABSTRACT

A method for allocating edge computing resources is performed by a computer device, the method including: obtaining computing power resource information occupied by a target application at an edge computing node and a current operating frequency of the edge computing node during operation of the target application; determining expected demand computing power resource information of the target application for the edge computing node within a target time period according to a number of newly added service objects, offline service objects, and scenario switching service objects during the target time period; and determining a target operating frequency of the edge computing node within the target time period according to the current operating frequency, the occupied computing power resource information, and the expected demand computing power resource information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2022/106177, entitled “DATA PROCESSING METHOD AND APPARATUSBASED ON EDGE COMPUTING, DEVICE, AND STORAGE MEDIUM” filed on Jul. 18,2022, which claims priority to Chinese Patent Application202110855560.5, entitled “DATA PROCESSING METHOD BASED ON EDGECOMPUTING, DEVICE, AND READABLE STORAGE MEDIUM” filed to the ChinesePatent Office on Jul. 28, 2021, which is incorporated herein byreference in its entirety.

FIELD OF THE TECHNOLOGY

This application relates to the field of computer technologies, and inparticular to a data processing method and apparatus based on edgecomputing, a computer device, and a computer-readable storage medium.

BACKGROUND OF THE DISCLOSURE

Cloud gaming is a process of running games on remote servers, andcompressing and encoding rendered game pictures and then sending same toterminals through a network in an audio and video stream manner. Cloudgaming does not need to consider terminal configurations, therebythoroughly solving the technical problem that terminals cannot run largegames due to insufficient performance. However, cloud gaming has veryhigh requirements on network delay. In order to provide a more stablenetwork condition for objects, edge computing nodes may generally bedeployed on a large scale, so that cloud gaming servers are closer tothe objects.

In order to provide a better experience for the objects, operatingfrequencies of the edge computing nodes are generally set according tothe largest quantity of online game objects, and the provided computingpower is prepared for the largest quantity of the online game objects.However, the quantity of the online game objects has a relativelyobvious tide phenomenon. In an off-peak period, the actual quantity ofthe online game objects is much less than the largest online quantity.In this case, the operating frequencies of the edge computing nodes areexcessively large in terms of the actual online quantity. The largepower consumption of the edge computing nodes causes waste of operationcosts of the edge computing nodes and shortening of the service life,and the operation costs are greatly increased.

SUMMARY

A method for allocating edge computing resources performed by a computerdevice is provided and the method includes:

-   -   obtaining computing power resource information occupied by a        target application at an edge computing node and a current        operating frequency of the edge computing node during operation        of the target application;    -   determining expected demand computing power resource information        of the target application for the edge computing node within a        target time period according to a number of newly added service        objects, offline service objects, and scenario switching service        objects during the target time period; and    -   determining a target operating frequency of the edge computing        node within the target time period according to the current        operating frequency, the occupied computing power resource        information, and the expected demand computing power resource        information.

A computer device includes: a processor, a memory, and a networkinterface, the processor being connected to the memory and the networkinterface, the memory storing computer-readable instructions, and thecomputer-readable instructions, when executed by the processor, causingthe computer device to implement the method according to embodiments ofthis application.

A non-transitory computer-readable storage medium, storescomputer-readable instructions, and the computer-readable instructions,when executed by a processor of a computer device, causing the computerdevice to implement the method according to embodiments of thisapplication.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of the presentdisclosure or the related art more clearly, the accompanying drawingsrequired for describing the embodiments or the related art are brieflydescribed below. Apparently, the accompanying drawings in the followingdescription show some embodiments of the present disclosure, and aperson skilled in the art may still derive other drawings from theseaccompanying drawings without creative efforts.

FIG. 1 is a schematic diagram of a network architecture providedaccording to an embodiment of this application.

FIG. 2A and FIG. 2B are scenario diagrams of predicting a targetoperating frequency within a target time period provided according toembodiments of this application.

FIG. 3 is a schematic flowchart of a data processing method based onedge computing provided according to an embodiment of this application.

FIG. 4 is a logic flowchart of adjusting an operating frequency of anedge computing node provided according to an embodiment of thisapplication.

FIG. 5 is a schematic flowchart of determining a target operatingfrequency within a target time period provided according to anembodiment of this application.

FIG. 6 is a diagram of a system architecture provided according to anembodiment of this application.

FIG. 7 is a schematic structural diagram of a data processing apparatusbased on edge computing provided according to an embodiment of thisapplication.

FIG. 8 is a schematic structural diagram of a computer device providedaccording to an embodiment of this application.

DESCRIPTION OF EMBODIMENTS

The technical solutions in embodiments of this application are clearlyand completely described below with reference to the accompanyingdrawings in the embodiments of this application. Apparently, thedescribed embodiments are merely some rather than all of the embodimentsof this application. All other embodiments obtained by a person skilledin the art based on the embodiments of this application without makingcreative efforts shall fall within the protection scope of thisapplication.

This application relates to technologies such as cloud computing, cloudgaming and edge computing. Related concepts of cloud computing, cloudgaming and edge computing are first described below.

Cloud computing refers to a delivery and use mode of an ITinfrastructure, which refers to obtaining required resources in anon-demand and easy-to-expand manner through a network. Broadly, cloudcomputing refers to a delivery and use mode of services, which refers toobtaining required services in an on-demand and easy-to-expand mannerthrough a network. Such services may be related to IT, software, and theInternet, or other services. Cloud computing is a product developed andfused by conventional computer and network technologies such as gridcomputing, distributed computing, parallel computing, utility computing,network storage technologies, virtualization, and load balance.

With the diversified development of the Internet, real-time data streamsand connection devices, and under the promotion of demands such assearch services, social networks, mobile commerce and opencollaboration, cloud computing rapidly develops. Unlike conventionalparallel distributed computing, the emergence of cloud computing willpromote a revolutionary change of the whole Internet mode and enterprisemanagement mode from the concept.

Cloud gaming, which may also be referred to as gaming on demand, is anonline gaming technology based on the cloud computing technology. Thecloud gaming technology enables a thin client with relatively limitedgraphics processing and data computing capabilities to run ahigh-quality game. In a cloud gaming scenario, the game does not run ina terminal of a game player, but runs in a cloud server, and the cloudserver renders a game scenario as a video/audio stream and transmitssame to the terminal of the game player through a network. The terminalof the game player does not need to have a powerful graphics operationand data processing capability, and only needs to have basic streamingmedia playing capability and the capability of obtaining a player inputinstruction and sending same to the cloud server.

Edge computing refers to an open platform integrating network,computing, storage and application core capabilities on one side closeto an object or a data source, and provides a nearest-end servicenearby. Application programs of edge computing are initiated at the edgeside, a faster network service response is generated, and basicrequirements in real-time services, application intelligence, security,privacy protection, and the like are satisfied.

A cloud gaming edge computing node is a node for edge computing, and isgenerally composed of a plurality of servers having a graphicsprocessing unit (GPU) computing capability. A single server may bereferred to as a computing node.

Computing power, as its name suggests, is the computing capability ofdevices such as small devices including mobile phones and computers, andlarge devices such as super computers. The computing power exists invarious hardware devices. Computing power resources are hardware ornetwork resources that need to be occupied when a device executes acomputing task, and may generally include a central processing unit(CPU) computing power resource, a GPU computing power resource, a memoryresource, a network bandwidth resource, and a disk resource.

The solutions provided in the embodiments of this application relate tocloud computing and cloud gaming technologies in the field of cloudtechnologies, and the specific processes are described in the followingembodiments.

FIG. 1 is a schematic diagram of a network architecture providedaccording to an embodiment of this application. As shown in FIG. 1 , thenetwork architecture may include a management server 100 and an edgenode 11, an edge node 12, . . . , and an edge node 1 n. The edge node 11may include a plurality of computing servers such as a computing server11 a and a computing server 11 b, and the edge node 12 may include aplurality of computing servers such as a computing server 12 a and acomputing server 12 b. As shown in FIG. 1 , the computing servers suchas the computing server 11 a and the computing server 11 b in the edgenode 11 may communicate with each other, and the computing servers suchas the computing server 12 a and the computing server 12 b in the edgenode 12 may communicate with each other. Any computing server in theedge node 11, any computing server in the edge node 12, . . . , and anycomputing server in the edge node In may respectively establish anetwork connection with the management server 100, so that eachcomputing server may perform data interaction with the management server100 through the network connection, and each computing server mayreceive management data from the management server 100. It may beunderstood that the computing servers in an edge node are generallydeployed in a same region, and different edge nodes are generallydeployed in different regions.

As shown in FIG. 1 , the computing servers in the edge nodes maycorrespond to a terminal device cluster, and a target application may beintegrally installed on each terminal device in the terminal devicecluster. When the target application runs in each terminal device, thetarget application may perform data interaction with a computing serverallocated by the management server 100 to the target application. Thetarget application may include one or more applications of a gameapplication, a video editing application, a social application, aninstant messaging application, a live streaming application, a shortvideo application, a video application, a music application, a shoppingapplication, a novel application, a payment application, a browser, andthe like which have data information functions such as text, image,audio, and video. A computing server provides a corresponding functionservice for a target application operating in a terminal device, butconsumes corresponding computing power resources at the same time. Thecomputing power resources of a computing server may correspond todifferent terminal devices at the same time. When a target application(e.g., a cloud gaming application) operates in the terminal deviceaccessing the computing server, the target application will occupy thecomputing power resources of the computing server. Each computing servermay also be referred to as an edge computing node.

In order to enable an object using a target application to smoothlyoperate the target application, the computing server generally preparescomputing power resources for the target application according to thelargest online quantity (the quantity of objects logging into the targetapplication) of the target application (setting an operating frequencyaccording to the largest online quantity, and preparing computing powerresources according to the operating frequency). The operating frequencymay refer to a frequency of an operation module such as a CPU and a GPUin the computing server during operation. For example, generally, thelargest online quantity of the target application is 500, then theoperating frequency set by the computing server for the targetapplication is a frequency capable of providing function services for500 objects, and prepared computing power resources are computing powerresources corresponding to the operating frequency. If the quantity ofonline objects of the target application is much less than the largestonline quantity 500 within a time period, the operating frequency of thecomputing server is excessively large (which is the operating frequencycorresponding to the largest online quantity 500), and actually, arelatively small operating frequency is required, thereby causing wasteof the computing power resources of the computing server, and alsocausing excessive power consumption of the computing server. Similarly,if the quantity of online objects of the target application is much morethan the largest online quantity 500 within a time period, functionservices that can be provided by the operating frequency of thecomputing server cannot support the operating of the target application,so that requirements of the target application cannot be satisfied.

In order to enable the computing server to better satisfy therequirements of the target application for computing power resourceswhile reducing the power consumption, the management server 100 performsdata interaction with each computing server to obtain a currentoperating frequency of each computing server, so as to obtain computingpower total resource information (i.e., the maximum computing powerresource information) corresponding to the current operating frequency.The management server 100 may also obtain occupied computing powerresource information of the computing server (i.e., computing powerresources of the computing server occupied by the target applicationduring operation, for example, CPU computing power resources of thecomputing server occupied by the target application during operation),and predict expected demand computing power resource information of thetarget application for the computing server within a target time period(i.e., computing power resources of the computing server that arepredicted to be occupied by the target application within the targettime period, for example, CPU computing power resources of the computingserver that are predicted to be occupied by the target applicationduring operation within the target time period). Subsequently, themanagement server 100 may determine a target operating frequency (thetarget operating frequency may refer to a predicted operating frequencyof the computing server within the target time period, the targetoperating frequency may be obtained after the current operatingfrequency is increased or reduced, and the target operating frequencymay also be equal to the current operating frequency) of the computingserver within the target time period according to the expected demandcomputing power resource information, the occupied computing powerresource information, and the maximum computing power resourceinformation corresponding to the current operating frequency (thecurrent operating frequency may refer to a frequency of an operationmodule such as a CPU and a GPU in the computing server during currentoperating; and when the occupied computing power resource informationand the expected demand computing power resource information refer toCPU computing power resources, the current operating frequency may referto a frequency of the CPU in the computing server during the currentoperating). The maximum computing power resource informationcorresponding to the target operating frequency satisfies the expecteddemand computing power resource information. That is to say, theoperating frequency of the computing server may be dynamically adjusted,so that the maximum computing power resource information correspondingto the target operating frequency of the computing server within thetarget time period may satisfy the expected demand computing powerresource information, thereby reducing a situation in which powerconsumption is excessively large or computing power resourcerequirements cannot be satisfied due to the fact that the operatingfrequency of the computing server within the target time period isexcessively large or small.

For ease of understanding, description is made by taking an example inwhich the management server 100 determines the target operatingfrequency of the computing server 11 a within the target time period. Asshown in FIG. 1 , the management server 100 obtains occupied computingpower resource information (i.e., computing power resource informationof the computing server 11 a occupied during operation of the targetapplication) of the target application for the computing server 11 a.The occupied computing power resource information may refer toquantitative index information of computing power resources occupiedwhen the computing server 11 a provides a function service for thetarget application. The quantitative index information may include oneor more of a plurality of pieces of index information such as centralprocessing unit (CPU) computing power information, graphics processingunit (GPU) computing power information, memory usage information,network bandwidth usage information, and disk read-write capabilityinformation. By taking an example in which the quantitative indexinformation includes the CPU computing power and the GPU computingpower, the occupied computing power resource information is calculating:the values of the CPU computing power and the GPU computing power of thecomputing server 11 a occupied during operation of the targetapplication.

Subsequently, the management server 100 may predict expected demandcomputing power resource information (i.e., computing power resourceinformation of the computing server 11 a that is predicted to beoccupied when the target application operates within the target timeperiod) of the target application for the computing server 11 a withinthe target time period. The management server 100 may obtain a currentoperating frequency of the computing server 11 a. The management server100 may determine a target operating frequency of the computing server11 a within the target time period according to the maximum computingpower resource information corresponding to the current operatingfrequency, the occupied computing power resource information, and theexpected demand computing power resource information. The maximumcomputing power resource information corresponding to the targetoperating frequency satisfies the expected demand computing powerresource information. Then, in the target time period, the operatingfrequency of the computing server 11 a may be adjusted from the currentoperating frequency to the target operating frequency, so that computingpower resources provided by the computing server 11 a for the targetapplication within the target time period are not excessively large orsmall. For specific implementation manners of predicting expected demandcomputing power resource information of the target application for thecomputing server 11 a within the target time period, and determining thetarget operating frequency within the target time period, please referto the description in the embodiment corresponding to subsequent FIG. 3.

It may be understood that the foregoing processing process may beperformed by the management server separately, or may be separatelyexecuted by the computing server, or may be executed by the managementserver and the computing server, and the specific implementation may beadjusted according to actual requirements, which is not limited herein.

It may be understood that the method provided according to theembodiments of this application may be executed by a computer device,and the computer device includes, but is not limited to, a terminaldevice, a computing server, or a management server. The managementserver may be an independent physical server or a server clustercomposed of a plurality of physical servers or a distributed system, ormay be a cloud server that provides basic cloud computing services suchas cloud services, cloud databases, cloud computing, cloud functions,cloud storage, network services, cloud communications, middlewareservices, domain name services, security services, CDN, and big data andartificial intelligence platforms.

The terminal device in the terminal device cluster may include a mobilephone, a tablet computer, a notebook computer, a handheld computer, asmart speaker, a mobile Internet device (MID), a point of sales (POS)machine, a wearable device (such as a smart watch and a smartwristband), a vehicle-mounted device, and the like.

For ease of understanding, please refer to FIG. 2A and FIG. 2B. FIG. 2Aand FIG. 2B are scenario diagrams of predicting a target operatingfrequency within a target time period provided according to embodimentsof this application. The scenario shown in FIG. 2A gives an example inwhich the target application is a cloud gaming application of amulti-player competition game. A service object A may operate the cloudgaming application through a corresponding terminal device 10 a, aservice object B may operate the cloud gaming application through acorresponding terminal device 10 b, . . . , and a service object N mayoperate the cloud gaming application through a corresponding terminaldevice 10 n. The computing server 11 b provides service support for thecloud gaming application in the terminal device 10 a, the terminaldevice 10 b, . . . , and the terminal device 10 n. The computing server11 b may also be referred to as a cloud game server or an edge computingnode.

It may be understood that the cloud gaming application may includedifferent application activity scenarios, and service objects (such as aservice object A, a service object B, . . . , and a service object N;the service object may refer to a binding account of a service userusing the terminal device to operate the cloud gaming application in thecloud gaming application; the service user may use the binding accountto log into the cloud gaming application; and the cloud gamingapplication may also determine, through the binding account, whether theservice user has logged in) logged into the cloud gaming application mayenter the different application activity scenarios, and perform games inthe different application activity scenarios. The computing powerresources required by the different application activity scenarios aregenerally different.

The application activity scenario may refer to a scenario type to whicha picture displayed on the terminal device belongs when the cloud gamingapplication operates on the terminal device. An application activityscenario may provide a corresponding application function for the cloudgaming application. For example, the application activity scenario mayinclude a game home lobby scenario (i.e., when a service user logs in oropens a cloud gaming application, the cloud gaming application generallypresents a default picture which can be used for game characterpresentation, character costume change, competition mode selection, andthe like; and the default presentation picture is generally referred toas a game home lobby), a single-player competition scenario (i.e., asingle-player mode in the cloud gaming application; the single-playercompetition scenario is used for single-player parachuting,single-player breakthrough, and the like), a multi-player competitionscenario (i.e., a multi-player competition mode in the cloud gamingapplication; the multi-player competition scenario may be used formulti-player team-forming to fight monster to increase the game level,multi-player team-forming breakthrough, and the like.), and the like.Generally, in the game home lobby scenario, simple pictures and controlssuch as a game background, game character introduction of a serviceobject, and game competition mode selection need to be presented to theservice object, and the game home lobby scenario requires smallcomputing power resources (the computing server 11 b may operate at asmall frequency). In the single-player competition scenario, the serviceobject may perform a single-player competition game (e.g., a singleplayer operates the game character to increase game experience or obtaina game gold coin and the like). Compared with the game home lobbyscenario, in the single-player competition scenario, there are moreoperation instructions for the service object, which requires morecalculation, and then the single-player competition scenario requiresmore computing power resources (the computing server 11 b needs tooperate at a larger frequency). In the multi-player competitionscenario, the service objects may perform a large number of gameoperations (such as sliding a steering wheel rim, releasing skills of agame character, and clicking a retreat control). In addition, voicecommunication, text communication, and the like may also be performedbetween the service objects. That is to say, generally, the multi-playercompetition scenario needs to satisfy game operation instructions of theservice objects, and also needs to provide computing services forcommunication to the service objects. Compared with the single-playercompetition game, the multi-player competition scenario requires morecomputing power resources (the computing server 11 b needs to operate ata larger frequency).

As shown in FIG. 2A, by taking an example in which the service object A,the service object B, . . . , and the service object N include a serviceobject 1, a service object 2, a service object 3, . . . , and a serviceobject 10, the service object 1, the service object 2, the serviceobject 3, . . . , and the service object 10 are all in an online state.The service object 1, the service object 2, the service object 3, . . ., and the service object 10 may be referred as current online serviceobjects. The management server 100 may obtain an application activityscenario where each service object of the service object 1, the serviceobject 2, the service object 3, . . . , and the service object 10 islocated, and obtain computing power resources (hereinafter referred toas service object occupied resource information) occupied when eachservice object is located in the corresponding application activityscenario. By taking an example in which the application activityscenario where the service objects 1-5 are located is the game lobbyscenario, the application activity scenario where the service objects6-8 are located is the single-player competition scenario, and theapplication activity scenario where the service objects 9-10 are locatedis the multi-player competition scenario, the service object occupiedresource information corresponding to the service object 1 is 70, theservice object occupied resource information corresponding to theservice object 2 is 71, the service object occupied resource informationcorresponding to the service object 3 is 72, the service object occupiedresource information corresponding to the service object 4 is 70, andthe service object occupied resource information corresponding to theservice object 5 is 73. In this case, the management server 100 may addthe service object occupied resource information corresponding to theservice objects 1-5, thereby obtaining the total occupied computingpower resources 356 (i.e., 70+71+72+70+73) of the game home lobbyscenario. In this embodiment, the unit of the service object occupiedresource information is TOPS, GOPS, or MOPS. TOPS is an abbreviation forTera operations per second, and 1 TOPS represents that a processor mayperform trillion (10{circumflex over ( )}12) operations per second. GOPSis an abbreviation for Giga operations per second, and 1 GOPS representsthat a processor may perform billion (10{circumflex over ( )}9)operations per second. MOPS is an abbreviation for million operation persecond, and 1 MOPS represents that a processor may perform million(10{circumflex over ( )}6) operations per second.

The service object occupied resource information corresponding to theservice object 6 is 101, the service object occupied resourceinformation corresponding to the service object 7 is 110, and theservice object occupied resource information corresponding to theservice object 8 is 108. In this case, the management server 100 may addthe service object occupied resource information corresponding to theservice objects 6-8, thereby obtaining the total occupied computingpower resources 319 (i.e., 101+110+108) of the single-player competitionscenario. If the service object occupied resource informationcorresponding to the service object 9 is 200, and the service objectoccupied resource information corresponding to the service object 10 is208, the management server 100 may add the service object occupiedresource information corresponding to the service objects 9-10, therebyobtaining the total occupied computing power resources 408 (i.e.,200+208) of the multi-player competition scenario. Further, themanagement server 100 may add the total occupied computing powerresources respectively corresponding to the game home lobby scenario,the single-player competition scenario, and the multi-player competitionscenario, thereby obtaining occupied computing power resourceinformation 1083 of the computing server 11 b.

Further, as shown in FIG. 2B, the management server 100 may predict anewly added service object (i.e., a binding account corresponding to aservice user who logs into or opens the cloud gaming application withinthe target time period; the newly added service object do not belong tothe current online service objects), an offline service object (i.e., abinding account corresponding to a service user in the current onlineservice objects who exits from the cloud gaming application within thetarget time period), and a scenario switching service object (i.e., abinding account corresponding to a service user in the current onlineservice objects who switches an application activity scenario within thetarget time period; the application activity scenario where thesescenario switching service objects are currently located is differentfrom the application activity scenario where the scenario switchingservice objects are located within the target time period, for example,the application activity scenario where the scenario switching serviceobjects are currently located is the game home lobby scenario, but theapplication activity scenario where the scenario switching serviceobjects are located within the target time period is the single-playercompetition scenario) for the cloud gaming application within the targettime period. By taking an example in which the newly added serviceobject within the target time period includes a service object 11 and aservice object 12 (both the service object 11 and the service object 12do not belong to the current online service objects 1-10), the offlineservice object includes the service object 7 and the service object 8 inthe current online service objects 1-10, and the scenario switchingservice object includes the service object 9 in the current onlineservice objects 1-10, generally, when the newly added service objectstarts to operate the cloud gaming application through the terminaldevice, a scenario displayed on the terminal device is generally thegame home lobby scenario. That is to say, when the service object 11 andthe service object 12 log into the cloud gaming application, an initiallogin scenario where the service object 11 and the service object 12 arelocated is the game home lobby scenario. In this case, first variablecomputing power resource information corresponding to the newly addedservice object may be determined according to the total occupiedcomputing power resources (i.e., 356) corresponding to the game homelobby scenario, the service object 11, and the service object 12. Thefirst variable computing power resource information is newly addeddemand computing power resource information corresponding to the serviceobject 11 and the service object 12.

The specific method for determining the first variable computing powerresource information includes: first, obtaining the total quantity(i.e., 5) of the service objects 1-5, and determining average computingpower demand information (i.e., 356/5=71.2) of the game home lobbyscenario according to the total occupied computing power resources(i.e., 356) corresponding to the game home lobby scenario and the totalquantity (i.e., 5) of the objects, the average computing power demandinformation being average computing power resource information requiredby each of the service objects 1-5 in the game home lobby scenario; andthen, obtaining the newly added quantity (i.e., 2) of the newly addedservice object (including the service object 11 and the service object12), and multiplying the average computing power demand information 71.2of the game home lobby scenario with the newly added quantity 2 toobtain the first variable computing power resource information 142.4.

It is to be understood that, the management server 100 may obtainapplication activity scenarios where the service object 7 and theservice object 8 having an offline behavior are respectively located (itcan be seen from the above that both the service object 7 and theservice object 8 are located in the single-player competition scenario).After the service object 7 and the service object 8 are offline, thecomputing server 11 b does not need to provide computing power resourcesthereto, and then the computing server 11 b may release thecorresponding computing power resources. In this case, the managementserver 100 may determine second variable computing power resourceinformation corresponding to the offline service object, and the secondvariable computing power resource information is computing powerresource information that is to be released and corresponds to theservice object 7 and the service object 8.

The specific method for determining the second variable computing powerresource information includes: first, obtaining the total quantity(i.e., 3) of the service objects 6-8, and determining average computingpower demand information (i.e., 319/3=106.3) of the single-playercompetition scenario according to the total occupied computing powerresources (i.e., 319) corresponding to the single-player competitionscenario and the total quantity (i.e., 3) of the objects, the averagecomputing power demand information being average computing powerresource information required by each of the service objects 6-8 in thesingle-player competition scenario; and then, obtaining the offlinequantity (i.e., 2) of the offline service object (including the serviceobject 7 and the service object 8), and multiplying the averagecomputing power demand information 106.3 of the single-playercompetition scenario with the offline quantity 2 to obtain the secondvariable computing power resource information 212.6.

It is to be understood that, the management server 100 may obtain anapplication activity scenario where the service object 9 having anscenario switching behavior is located (it can be seen from the abovethat the scenario where the service object 9 is located is themulti-player competition scenario), and an application activity scenariowhere the service object 9 is located after the scenario switching(e.g., the scenario where the service object 9 is located after theswitching is the game home lobby scenario). Since different applicationactivity scenarios require different computing power resources, afterthe service object 9 performs scenario switching, the computing powerresources required by the service object 9 will also change. In thiscase, the management server 100 may determine third variable computingpower resource information corresponding to the service object 9. Thethird variable computing power resource information is variablecomputing power resource information after scenario switching (thechange computing power resource information may be a positive value or anegative value; when the variable computing power resource informationis a positive value, it is indicated that the computing server 11 bneeds to newly add computing power resources after scenario switching;and when the variable computing power resource information is a negativevalue, it is indicated that the computing server 11 b needs to releasecomputing power resources after scenario switching).

The specific method for determining the third variable computing powerresource information includes: first, obtaining the total quantity(i.e., 2) of the service objects 9-10, and determining average computingpower demand information (i.e., 408/2=204) of the multi-playercompetition scenario according to the total occupied computing powerresources (i.e., 408) corresponding to the multi-player competitionscenario and the total quantity (i.e., 2) of the objects, the averagecomputing power demand information being average computing powerresource information required by each of the service objects 9-10 in themulti-player competition scenario; and then, obtaining the switchingquantity (i.e., 1) of the scenario switching service object (includingthe service object 9), and multiplying the average computing powerdemand information 204 of the multi-player competition scenario with theswitching quantity 1 to obtain computing power resources 204 that are tobe released by the multi-player competition scenario. Then, it can beseen from the above that the average computing power demand informationof the game home lobby scenario is 71.2. The management server 100 maymultiply the average computing power demand information 71.2 of the gamehome lobby scenario with the switching quantity 1, thereby obtainingdemand computing power resources 71.2 that are to be increased by thegame home lobby scenario. It can be seen from the above that, sincecomputing power resources that are to be released by the service object9 after scenario switching are greater than computing power resources tobe increased, the total variable computing power resource information(i.e., the third variable computing power resource information)corresponding to the service object 9 is −132.8 (i.e., 71.2−204).

Further, the management server 100 may add the determined occupiedcomputing power resource information (1083), the first variablecomputing power resource information (i.e., 142.4), and the thirdvariable computing power resource information (i.e., −132.8), andsubtracts the second variable computing power resource information(i.e., 212.6) from the result obtained by addition (1092.6). Theobtained result (i.e., 880) can be determined as expected demandcomputing power resource information required by the cloud gamingapplication for the computing server 11 b within the target time period.

Further, the management server 100 may obtain a current operatingfrequency of the computing server 11 b, and determine the currentmaximum computing power resource information of the computing server 11b according to the current operating frequency. The management server100 may determine a target operating frequency of the computing server11 b within the target time period according to the maximum computingpower resource information corresponding to the current operatingfrequency, the expected demand power resource information, and theoccupied computing power resource information. The maximum computingpower resource information corresponding to the target operatingfrequency satisfies the expected demand computing power resourceinformation. For a specific implementation of determining the targetoperating frequency, please refer to the description in the embodimentcorresponding to subsequence FIG. 4 .

After the target operating frequency is determined, the operatingfrequency of the computing server 11 b within the target time period maybe adjusted from the current operating frequency to the target operatingfrequency. The operating frequency of the computing server isdynamically adjusted, so that the maximum computing power resourceinformation corresponding to the target operating frequency of thecomputing server 11 b within the target time period may satisfy theexpected demand computing power resource information, thereby reducing asituation in which power consumption of the computing server 11 b isexcessively large or computing power resource requirements of the cloudgaming application cannot be satisfied due to the fact that theoperating frequency of the computing server within the target timeperiod is excessively large or small.

The numerical values of the computing power resources (e.g., 71, 70,110, 108, etc.) are all illustrated for ease of understanding, and donot have practical reference significance.

Further, FIG. 3 is a schematic flowchart of a data processing methodbased on edge computing provided according to an embodiment of thisapplication. The method may be executed by the computer device in theembodiment corresponding to FIG. 1 , that is, may be executed by themanagement server 100 in FIG. 1 , or may be executed by a computingserver (also including a computing server 11 a, a computing server 11 b,a computing server 12 a, and a computing server 12 b) in the edge nodecluster in FIG. 1 . As shown in FIG. 3 , the data processing methodbased on edge computing may include the following steps S101 to S103:

Step S101: Obtain occupied computing power resource information of atarget application for an edge computing node. The occupied computingpower resource information is computing power resource information ofthe edge computing node occupied during operation of the targetapplication.

In this application, the edge computing node may refer to a computerdevice capable of providing computing or application services, such as aserver (e.g., the computing server 11 a, the computing server 11 b, thecomputing server 12 a, or the computing server 12 b shown in FIG. 1 ).The target application may refer to an application for which the edgecomputing node needs to complete a related computing task, for example,the target application may be a cloud gaming application. On the basisof the cloud computing technology, cloud gaming generally operates on aremote server, and a terminal device only needs to receive anaudio/video stream sent by the remote server and then decodes and playsthe audio/video stream. In this case, the remote server is an edgecomputing node.

It is to be understood that when the target application operates on theterminal device, the edge computing node may provide correspondingcomputing services for the target application, and when the edgecomputing node provides corresponding computing services for the targetapplication, the target application occupies computing power resourcesof the edge computing node. The computing power may refer to thecomputing capability of the edge computing node. In this application,the measurement of the computing power of the edge computing nodegenerally uses the CPU computing power and the GPU computing power. TheCPU computing power is generally measured using operations per second(OPS). The GPU computing power has a plurality of measurement indexesaccording to the operation types, and is generally measured from thecomputing capability and data read throughput. The computing capabilitymay be measured from floating-point operations per second (FLOPS), OPS,half-precision peak operation capability, and dual-precision peakoperation capability according to the operation types. For computingpower resources, certainly, other computing power resources, such asmemory resources, network bandwidth resources, and disk resources, mayalso be included in addition to CPU computing power resources and GPUcomputing power resources. This application does not limit the contentincluded in the computing power resources. Description is made below bytaking an example in which the computing power resources include CPUcomputing power resources and GPU computing power resources.

Specifically, the target application may include one or more applicationactivity scenarios. The application activity scenario may refer to ascenario type to which a picture displayed on the terminal devicebelongs when the target application operates on the terminal device. Anapplication activity scenario may provide a corresponding applicationfunction for the target application. For example, when the targetapplication is a social application, the application activity scenariomay include a voice call scenario, a video call scenario, and a textchat scenario. After a service user logs in the social applicationthrough a binding account (referred to as a service object) of thesocial application, in a voice call scenario, the social applicationprovides a voice communication function between different serviceobjects (i.e., voice communication may be performed between the serviceusers). In a video call scenario, the social application provides avideo communication function between different service objects (i.e.,video communication may be performed between the service users). In atext chat scenario, the social application provides a text communicationfunction between different service objects (i.e., text communication maybe performed between the service users). For example, when the targetapplication is a cloud gaming application, the application activityscenario may include a game home lobby scenario, a single-playercompetition scenario, and a multi-player competition scenario. In thegame home lobby scenario, the cloud gaming application providesfunctions of game background introduction, game character introduction,data display, competitive mode selection and the like for the serviceobjects. In the single-player competition scenario, the cloud gamingapplication provides a single-player competition function for theservice objects. In the multi-player competition scenario, the cloudgaming application provides a multi-player competition function, amulti-player communication function and the like for the serviceobjects.

It may be understood that when the application activity scenarios of theservice objects in the target application are different, requiredcomputing power resources are also generally different. Therefore, inthis application, an application activity scenario of an online serviceobject in a target application may be obtained, and computing powerresources occupied by each online service object in the applicationactivity scenario of the online service object may be obtained, so thatthe computing power resources of edge computing nodes occupied by allthe online service objects may be obtained through statistics. Thecomputing power resources of the edge computing nodes occupied by allthe online service objects may be referred to as occupied computingpower resource information. The specific method for determining occupiedcomputing power resource information of the edge computing nodeincludes: obtaining N pieces of current operating information of thetarget application, one piece of current operating information includingan application activity scenario where an online service object islocated, and service object occupied resource information in theapplication activity scenario; obtaining service object occupiedresource information corresponding to each piece of current operatinginformation, to obtain N pieces of service object occupied resourceinformation; and determining a sum of the N pieces of service objectoccupied resource information as the occupied computing power resourceinformation of the target application for the edge computing node.

It is to be understood that the service object occupied resourceinformation may be understood as computing power resources of an edgecomputing node occupied when an online service object is in a certainapplication activity scenario. After each service user logs in thetarget application through the binding account (hereinafter referred toas a service object), a login state of the service object in the targetapplication is an online state. In this case, the service object mayalso be referred to as an online service object. The computer deviceobtains an application activity scenario of each online service objectin the target application in real time, and collects statistics aboutcomputing power resources occupied when the online service object is ina certain application activity scenario. One piece of current operatinginformation may be formed by a current application activity scenario ofan online service object and computing power resources occupied when theonline service object is located in the application activity scenario.One piece of current operating information includes an applicationactivity scenario where an online service object is located, andcomputing power resources occupied when the online service object islocated in the application activity scenario. Therefore, N pieces ofcurrent operating information may include application activity scenarioswhere N online service objects are respectively located, and computingpower resources respectively occupied when the N online service objectsare located in the corresponding application activity scenarios. Thetotal computing power resources (i.e., the occupied computing powerresource information) of the edge computing nodes occupied by the Nonline service objects may be obtained by adding and summing the Npieces of service object occupied resource information (i.e., thecomputing power resources respectively occupied when the N onlineservice objects are located in the corresponding application activityscenarios).

It is to be understood that the computing power resources include CPUcomputing power resources and GPU computing power resources. Therefore,when the occupied computing power resource information is subjected tostatistics collection, CPU computing power resources and GPU computingpower resources of the edge computing nodes occupied by the N onlineservice objects may be subjected to statistics collection. The occupiedCPU computing power resources and GPU computing power resources may bereferred as occupied computing power resource information.

Step S102: Predict a newly added service object, an offline serviceobject, and a scenario switching service object for the targetapplication within a target time period, and determine expected demandcomputing power resource information of the target application for theedge computing node within the target time period according to the newlyadded service object, the offline service object, and the scenarioswitching service object.

In this application, the newly added service object may refer to abinding account (a service object) of a service user starting the targetapplication in the target application within the target time period, andthe edge computing node may provide a function service thereto. Theoffline service object may refer to a service object corresponding tothe service user closing the target application within the target timeperiod among the service users corresponding to the N online serviceobjects. The scenario switching service object may refer to a serviceobject who switches an application activity scenario within the targettime period among the N online service objects (e.g., for a cloud gamingapplication, a certain online service object is in a single-playercompetition scenario at a current time point, and exits thesingle-player competition scenario and enters a game home lobby scenariowithin a target time period, and the online service object may bereferred to as a scenario switching object). In this application, anewly added service object, an offline service object, and a scenarioswitching service object within a target time period may be predicted,and expected demand computing power resource information within thetarget time period is determined according to the newly added serviceobject, the offline service object, and the scenario switching serviceobject.

The specific method for predicting a newly added service object, anoffline service object, and a scenario switching service object within atarget time period includes: the computer device obtains historicalservice behavior data of the target application, and then obtainsoperational activity information of the target application within thetarget time period; a newly added service object of the edge computingnode for the target application within the target time period isdetermined according to the operational activity information within thetarget time period and the historical service behavior data; and anoffline service object and a scenario switching service object of theedge computing node for the target application within the target timeperiod are determined according to the application activity scenariowhere the service object is located, the operational activityinformation within the target time period, and the historical servicebehavior data. The historical service behavior data may include relatedbehavior data of a historical online service object, a historical onlineservice object, a historical offline service object, and a historicalscenario switching service object of each time node of the targetapplication within a historical time period. The related behavior datamay include an application activity scenario, an operation behavior, anapplication operating duration, etc.

It is to be understood that the operational activity information withinthe target time period may refer to special activities released in thetarget application within a time period such as a holiday (such asLabour Day, Double Seventh Day, and Dragon Boat Festival), a specificfestival, and a version release date. For example, by taking an examplein which a cloud gaming application is the target application, in theDragon Boat Festival, a new Dragon Boat activity (such as a gamecharacter race activity) and a character costume time-limited purchaseactivity are released in the cloud gaming application. The serviceobjects probably will, according to the operational activityinformation, select to log into the cloud gaming application within thetarget time period to participate in the Dragon Boat activity, and topurchase character costumes. The service objects who do not log into thecloud gaming application (i.e., do not belong to N online serviceobjects) when the occupied computing power resource information issubjected to statistics collection, but are predicted to log into thecloud gaming application within the target time period may be referredto as newly added service objects. Moreover, the computer device maypredict, from the N online service objects according to the operationalactivity information and the historical service behavior data, serviceobjects who will stop operating the target application within the targettime period. The service objects may be referred to as offline serviceobjects. The computer device may also predict, from the N online serviceobjects according to the operational activity information and thehistorical service behavior data, service objects who will switch ascenario. The service objects may be referred to as scenario switchingservice objects.

In some embodiments, when the computer device predicts an offlineservice user of the edge computing node for the target applicationwithin the target time period, the computer device may generate,according to an offline prediction model, an application activityscenario where the service user is located, operational activityinformation corresponding to the target application, and an offlinebehavior feature when the service user has an offline behavior, thenoutputs a predicted offline label corresponding to the offline behaviorfeature in the offline prediction model, and then determines the offlineservice user according to the predicted offline label. The offlineprediction model is a machine learning model obtained by trainingaccording to the historical service behavior data, and is used forsimulating offline behaviors of the service user within different timeperiods, and speculating that the user will be offline in what kind ofbehavior state such as in what kind of activity scenario and what kindof time node. Correspondingly, the computer device may also predict ascenario switching service user by using a corresponding machinelearning model.

Further, expected demand computing power resource information of thetarget application for the edge computing node within the target timeperiod may be determined according to the newly added service object,the offline service object, and the scenario switching service object.By taking an example in which the N pieces of current operatinginformation include one or more application activity scenarios, and theone or more application activity scenarios include an applicationactivity scenario M_(i) (i is a positive integer), the specific methodfor determining the expected demand computing power resource informationis described. The specific method may include: determining, from Nonline service objects, an online service object, of which anapplication activity scenario is an application activity scenario M_(i),as a service object to be subjected to statistics collection; then,collecting statistics about total service object occupied resourceinformation of the service object to be subjected to statisticscollection in the application activity scenario M_(i); then, obtainingan object quantity corresponding to the service object to be subjectedto statistics collection, and determining, according to the totalservice object occupied resource information and the object quantity,average computing power demand information corresponding to theapplication activity scenario M_(i); and in response to determiningaverage computing power demand information respectively corresponding tothe one or more application activity scenarios, determining expecteddemand computing power resource information of the target applicationfor the edge computing node within the target time period according tothe average computing power demand information respectivelycorresponding to the one or more application activity scenarios, thenewly added service object, the offline service object, and the scenarioswitching service object.

The specific method for determining expected demand computing powerresource information of the target application for the edge computingnode within the target time period according to the average computingpower demand information respectively corresponding to the one or moreapplication activity scenarios, the newly added service object, theoffline service object, and the scenario switching service objectincludes: obtaining a newly added quantity corresponding to the newlyadded service object, an offline quantity corresponding to the offlineservice object, and a switching quantity corresponding to the scenarioswitching object; predicting an initial login scenario corresponding tothe newly added service object; the one or more application activityscenarios including the initial login scenario; then, determining anapplication activity scenario where the offline service object having anoffline behavior is located as an offline application activity scenario;then, determining an application activity scenario where the scenarioswitching object is located before scenario switching as an initialapplication activity scenario, and determining an application activityscenario where the scenario switching object is located after scenarioswitching as a target application activity scenario; and determiningexpected demand computing power resource information of the targetapplication for the edge computing node within the target time periodaccording to the average computing power demand information respectivelycorresponding to the initial login scenario, the offline applicationactivity scenario, the initial application activity scenario, and thetarget application activity scenario, as well as the switching quantity,the offline quantity, and the newly added quantity.

The specific method for determining expected demand computing powerresource information of the target application for the edge computingnode within the target time period according to the average computingpower demand information respectively corresponding to the initial loginscenario, the offline application activity scenario, the initialapplication activity scenario, and the target application activityscenario, as well as the switching quantity, the offline quantity, andthe newly added quantity includes: determining, according to newly addedquantity and the average computing power demand informationcorresponding to the initial login scenario, first variable computingpower resource information corresponding to the newly added serviceobject; determining second variable computing power resource informationcorresponding to the offline service object according to the offlinequantity and the average computing power demand informationcorresponding to the online application activity scenario; determiningthird variable computing power resource information corresponding to thescenario switching object according to the average computing powerdemand information corresponding to the initial application activityscenario, the average computing power demand information correspondingto the target application activity scenario, and the switching quantity;and determining expected demand computing power resource information ofthe target application for the edge computing node within the targettime period according to the first variable computing power resourceinformation, the second variable computing power resource information,the third variable computing power resource information, and theoccupied computing power resource information of the edge computingnode.

It is to be understood that when a service object logs in or opens atarget application, the service object generally enters a defaultinterface, and a scenario corresponding to the default interface may bereferred to as an initial login scenario. The one or more applicationactivity scenarios included in the target application scenarios includethe initial login scenario. For example, when a service object logs in acloud gaming application, the service object generally enters a gamehome lobby scenario, and the game home lobby scenario may be referred toas an initial login scenario. The computer device may obtain averagecomputing power demand information corresponding to the initial loginscenario (i.e., in the initial login scenario, average computing powerresources required by each online service object), and determine newlyadded computing power resource information corresponding to the newlyadded service object (e.g., a product result of the newly added quantityand the average computing power demand information corresponding to theinitial login scenario) according to the newly added quantity of thenewly added service object and the average computing power demandinformation corresponding to the initial login scenario (e.g.,multiplying the newly added quantity with the average computing powerdemand information corresponding to the initial login scenario). Thenewly added computing power resource information may be computing powerresources that need to be added when the edge computing node needs toprovide a function service for the newly added service object. The newlyadded computing power resource information may be referred as firstvariable computing power resource information.

It is to be understood that the computer device may also obtain anapplication activity scenario (hereinafter referred as an offlineapplication activity scenario) where the offline service object havingan offline behavior (e.g., a behavior of exiting or closing the targetapplication) is located, and determine released computing power resourceinformation corresponding to the offline service object according to theaverage computing power demand information corresponding to the offlineapplication activity scenario (i.e., in the offline application activityscenario, average computing power resources required by each onlineservice object), and the offline quantity corresponding to the offlineservice object. That is to say, when the offline service users close thetarget application, the edge computing node does not need to providefunction services for the offline service users, and the correspondingcomputing power resources may be released. These released computingpower resources may be referred as second variable computing powerresource information. The second variable computing power resourceinformation may be a product of the offline quantity and the averagecomputing power demand information corresponding to the offlineapplication activity scenario.

It is to be understood that since different application activityscenarios require different computing power resources, after the serviceobject performs scenario switching, the required computing powerresources will also change (may increase or increase). Therefore, thecomputer device may predict service objects who will perform scenarioswitching within the target time period among the N online serviceobjects, and predict application activity scenarios (referred as initialapplication activity scenarios) where the scenario switching objects arelocated before scenario switching, and application activity scenarios(referred as target application activity scenarios) where the scenarioswitching objects are located after scenario switching. For the initialapplication activity scenarios, the edge computing node may stopproviding corresponding computing power resources for the scenarioswitching users, and the corresponding computing power resources arereleased. For the target application activity scenarios, the edgecomputing node needs to provide corresponding computing power resources,and computing power resources are newly added. Therefore, the averagecomputing power demand information corresponding to the initialapplication activity scenario (in the initial application activityscenario, average computing power resources required by each onlineservice object), and the average computing power demand informationcorresponding to the target application activity scenario may beobtained. A product of the average computing power demand informationcorresponding to the initial application activity scenario and theswitching quantity of the scenario switching user is determined ascomputing power resources to be released corresponding to the initialapplication activity scenario. A product of the average computing powerdemand information corresponding to the target application activityscenario (in the target application activity scenario, average computingpower resources required by each online service object) and theswitching quantity of the scenario switching user is determined ascomputing power resources to be newly added corresponding to the targetapplication activity scenario. A result obtained by adding the computingpower resources to be released corresponding to the initial applicationactivity scenario and the computing power resources to be newly addedcorresponding to the target application activity scenario serves asthird variable computing power resource information (possibly a positivevalue or a negative value) corresponding to the scenario switching user.

Further, the occupied computing power resource information and the newlyadded computing power resources are added, and the computing powerresources to be released are subtracted from the result obtained byaddition, so as to obtain final expected computing power resourceinformation. That is, the occupied computing power resource information,the first variable computing power resource information, and the thirdvariable computing power resource information are added, and the secondvariable computing power resource information is subtracted from theresult obtained by addition, so as to obtain expected demand computingpower resource information of the target application for the edgecomputing node within the target time period.

Step S103: Obtain a current operating frequency of the edge computingnode, and determine a target operating frequency of the edge computingnode within the target time period according to maximum computing powerresource information corresponding to the current operating frequency,the occupied computing power resource information, and the expecteddemand computing power resource information. The maximum computing powerresource information corresponding to target operating frequencysatisfies the expected demand computing power resource information.

In this application, the computer device may obtain a current operatingfrequency of the edge computing node, and then may obtain maximumcomputing power resource information corresponding to the currentoperating frequency according to the current operating frequency. Atarget operating frequency of the edge computing node within the targettime period is determined according to the maximum computing powerresource information corresponding to the current operating frequency,the occupied computing power resource information, and the expecteddemand computing power resource information. Determining the targetoperating frequency of the edge computing node is determining whetherthe current operating frequency is to be increased or reduced, or toremain unchanged within the target time period. For ease ofunderstanding, please refer to FIG. 4 . FIG. 4 is a logic flowchart ofadjusting an operating frequency of an edge computing node providedaccording to an embodiment of this application. As shown in FIG. 4 , thelogic flow may include at least the following steps S41 to S49:

Step S41: Determine whether the computing power of the edge computingnode is fully loaded.

Specifically, the occupied computing power resource information of theedge computing node may be compared with the maximum computing powerresource information corresponding to the current operating frequency.When the occupied computing power resource information is less than themaximum computing power resource information corresponding to thecurrent operating frequency, and an absolute value of a differencebetween the occupied computing power resource information and themaximum computing power resource information corresponding to thecurrent operating frequency is greater than a threshold (which may bemanually set), it can be determined that the computing power of the edgecomputing node is not fully loaded, and there are redundant idlecomputing power resources. When the occupied computing power resourceinformation is less than the maximum computing power resourceinformation corresponding to the current operating frequency, but theabsolute value of the difference between the occupied computing powerresource information and the maximum computing power resourceinformation corresponding to the current operating frequency is lessthan the threshold, it can be determined that the computing power of theedge computing node is in a full-load state, and there are no redundantidle computing power resources. Optically, when the occupied computingpower resource information is equal to the maximum computing powerresource information corresponding to the current operating frequency,it can be determined that the computing power of the edge computing nodeis in a full-load state, and there are no redundant idle computing powerresources.

When it is determined that the computing power of the edge computingnode is in a full-load state, subsequence step S42 is executed. When itis determined that the computing power of the edge computing node is notin a full-load state (i.e., a non-full-load state), subsequence step S45is executed.

Step S42: Determine whether an expected computing power is increased.

Specifically, the occupied computing power resource information may becompared with the expected demand computing power resource information,so as to determine whether the expected demand computing power resourceinformation is increased or reduced (or remains unchanged) with respectto the occupied computing power resource information. When the expecteddemand computing power resource information is increased, subsequentstep S44 is executed. When the expected demand computing power resourceinformation is not increased, subsequent step S43 is executed.

Step S43: Maintain the current operating frequency unchanged.

Specifically, when the computing power of the edge computing node is afull-load state, and the expected demand computing power resourceinformation is not increased, it is indicated that the computing powerresources provided by the edge computing node at the current operatingfrequency satisfy the expected demand computing power resources. Inorder to reduce the adjustment frequency of the operating frequency, andreduce loss caused by the adjustment, the operating frequency can bemaintained unchanged. The unchanged current operating frequency is thetarget operating frequency within the target time period.

In some embodiments, the current operating frequency may also beappropriately reduced (e.g., reduced to the operating frequencycorresponding to the expected demand computing power resources).

Step S44: Increase the operating frequency.

Specifically, when the computing power of the edge computing node is afull-load state, and the expected demand computing power resourceinformation is increased, it is indicated that the computing powerresources provided by the edge computing node at the current operatingfrequency cannot satisfy the expected demand computing power resources.The operating frequency may be appropriately increased (e.g., increasedto the operating frequency corresponding to the expected demandcomputing power resources). The increased operating frequency is thetarget operating frequency within the target time period.

Step S45: Determine whether an expected computing power is increased.

Specifically, when it is determined that the computing power of the edgecomputing node is in a non-full-load state, the occupied computing powerresource information may be compared with the expected demand computingpower resource information, so as to determine whether the expecteddemand computing power resource information is increased or reduced (orremains unchanged) with respect to the occupied computing power resourceinformation. When the expected demand computing power resourceinformation is increased, subsequent step S46 is executed. When theexpected demand computing power resource information is not increased,subsequent step S48 is executed.

Step S46: Determine whether idle computing power satisfies expectedcomputing power.

Specifically, when it is determined that the computing power of the edgecomputing node is in a non-full-load state, and the expected demandcomputing power resource information is increased, the occupiedcomputing power resource information may be subtracted from the maximumcomputing power resource information corresponding to the currentoperating frequency, so as to obtain idle computing power resourceinformation of the edge computing node. Then, a resource sum of thefirst variable computing power resource information, the second variablecomputing power resource information, and the third variable computingpower resource information is subjected to statistics collection, andthe sum is demand computing power resources needing to be newly added bythe edge computing node. The idle computing power resource informationmay be compared with the resource sum. When the idle computing powerresource information is greater than the resource sum, it is determinedthat the idle computing power resource information may satisfy newlyadded demand computing power resources, and step S43 is executed (i.e.,the current operating frequency is maintained unchanged). When the idlecomputing power resource information is less than the resource sum, itis determined that the idle computing power resource information cannotsatisfy newly added demand computing power resources, and subsequentstep S47 is executed. In some embodiments, when the idle computing powerresource information is equal than the resource sum, it is determinedthat the idle computing power resource information may satisfy newlyadded demand computing power resources, and step S43 is executed.

Step S47: Increase the operating frequency.

Specifically, when the idle computing power resource information cannotsatisfy newly added demand computing power resources, the operatingfrequency may be appropriately increased (e.g., increased to theoperating frequency corresponding to the expected demand computing powerresources within the target time period). The increased operatingfrequency is the target operating frequency within the target timeperiod.

Step S48: Determine whether power consumption after frequency reductionchanges.

Specifically, when it is determined that the computing power of the edgecomputing node is in a non-full-load state, and the expected demandcomputing power resource information is not increased, the maximumcomputing power resource information corresponding to the currentoperating frequency may satisfy the expected computing power resourceinformation, and the operating frequency may be appropriately reduced.However, in this application, in order to reduce the adjustmentfrequency of the operating frequency (to avoid frequent adjustment ofthe operating frequency), and reduce the problem of unstable systemoperation caused by frequent frequency adjustment, before frequencyreduction, whether power consumption of the edge computing node afterfrequency reduction changes (i.e., whether the power consumption isreduced) may be first determined, and when there is a change in powerconsumption, the operating frequency is reduced. When there is no changein power consumption, the frequency may be maintained unchanged.Although the operating frequency is not reduced, frequent adjustment ofthe operating frequency is avoided in the whole frequency adjustmentprocess (which is not a situation in which when the frequency needs tobe reduced, the operating frequency is necessarily reduced), therebyimproving the stability of system operation, and also reducing operationcosts.

However, because the reduction of the operating frequency and thereduction of power consumption are not in a linear relationship (whichis not a situation in which the operating frequency is reduced, thepower consumption is necessarily reduced). Due to a difference inhardware implementation, when the operating frequency is slightlyreduced, the power consumption may not be reduced. Therefore, in orderto accurately collect statistics about a relationship betweenfrequencies and power consumption of hardware of different models, acorresponding relationship between the operating frequency and the powerconsumption can be continuously corrected according to historical dataand a real-time operation condition, so that a relationship mappingtable of the computing power, frequency and power consumption of thehardware (including the CPU and the GPU) can be obtained.

The relationship mapping table may be as shown in Table 1 below.

TABLE 1 Field Type Description Model String Machine model GPU modelString GPU model GPU operating int GPU operating frequency (Hz)frequency GPU maximum list Measurement through OPS and FLOPS computingpower GPU power int GPU power consumption (Watts) consumption CPU modelString CPU model CPU operating int GPU operating frequency (Hz)frequency CPU maximum List Measurement through OPS and FLOPS computingpower CPU power int CPU power consumption (Watts) consumption Machineint The unit of power is Watts. overall power Different CPU and GPUoperating consumption frequencies will cause heat dissipation andoperation of other components to be changed. In addition to changes inCPU and GPU power consumption, the power consumption of the overall unitwill also be significantly affected.

As shown in Table 1, the relationship mapping table may include amapping relationship among the operating frequency of the CPU, themaximum computing power corresponding to the operating frequency, andthe power consumption corresponding to the operating frequency. Therelationship mapping table may also include a mapping relationship amongthe operating frequency of the GPU, the maximum computing powercorresponding to the operating frequency, and the power consumptioncorresponding to the operating frequency. When the expected demandcomputing power resource information within the target time period isdetermined, the operating frequency corresponding to the expected demandcomputing power resource information may be obtained according to therelationship mapping table. When the computing power of the edgecomputing node is in a non-full-load state and the expected demandcomputing power resources are not increased, the operating frequency maybe appropriately reduced, but before this, whether the power consumptionis reduced when the frequency is reduced may be determined by means ofthe relationship mapping table. If compared with the power consumptioncorresponding to the current operating frequency, the power consumptioncorresponding to the expected demand computing power resourceinformation after frequency reduction is not reduced, the operatingfrequency may not be reduced, and the operating frequency can bemaintained unchanged. The current operating frequency is continued to betaken as the target operating frequency within the target time period.

If compared with the power consumption corresponding to the currentoperating frequency, the power consumption corresponding to the expecteddemand computing power resource information after frequency reduction ischanged (e.g., reduced), the operating frequency may be reduced (e.g.,through the relationship mapping table, the operating frequency isreduced to the operating frequency corresponding to the expected demandcomputing power resources). The reduced operating frequency may be takenas the target operating frequency within the target time period.

It is to be understood that, in this application, by determining whetherthere is a change in power consumption, the operating frequency of theedge computing node is dynamically adjusted, which may reduce asituation in which the operating frequency is frequently adjusted but nosignificant effect is realized, and can also improve the stability ofsystem operation.

In the embodiments of this application, the main factors affecting thecomputing power resource change of the edge computing node include thefactors that the object is online, gets offline, and gets online.Therefore, in this application, a newly added service object, an offlineservice object, and a scenario switching service object for the targetapplication within a target time period may be predicted, and expecteddemand computing power resource information of the target applicationfor the edge computing node within the target time period may bedetermined according to the newly added service object, the offlineservice object, and the scenario switching service object. Therefore,whether the current operating frequency is increased, reduced, ormaintained unchanged within the target time period may be determinedaccording to the expected demand computing power resource information ofthe target time period, the occupied computing power resourceinformation of the edge computing node, and the maximum computing powerresource information corresponding to the current operating frequency ofthe edge computing node (i.e., determining the target operatingfrequency within the target time period). The maximum computing powerresource information of the edge computing node corresponds to theoperating frequency of the edge computing node (e.g., the greater theoperating frequency, the greater the maximum computing power resourceinformation), and the operating frequency and the operating consumption(i.e., power consumption) of the edge computing node also have acorresponding relationship. Therefore, by adjusting the operatingfrequency of the edge computing node in a timely fashion (increased,reduced or maintained unchanged), the maximum computing power resourceinformation corresponding to the target operating frequency within thetarget time period can satisfy the expected demand computing powerresource information, so that the problem that the computing powerresources provided by the edge computing node are not matched withactually required computing power resources can be reduced (e.g., theproblem that the computing power resources provided by the edgecomputing node are excessively large but the actually required computingpower resources are small is reduced), the computing power resourcesprovided by the edge computing node are matched with the requiredcomputing power resources within the target time period, the operatingconsumption of the edge computing node cannot be excessively large, andthe operation costs are reduced. That is, in this application, byadjusting the operating frequency of the edge computing node, thecomputing power resources provided by the edge computing node will notbe much greater than the expected demand computing power resourceinformation (i.e., reducing the operation costs), and the computingpower resources will not be much less than the expected demand computingpower resource information so that the demand cannot be satisfied,thereby dynamically balancing the computing power demand and theoperation costs. That is, in this application, the operation costs ofthe edge computing node can be reduced while the computing power demandis satisfied.

Further, FIG. 5 is a schematic flowchart of determining a targetoperating frequency within a target time period provided according to anembodiment of this application. The flow may correspond to theembodiment corresponding to FIG. 3 , and step S103 is the flow ofdetermining the target operating frequency. As shown in FIG. 5 , theflow may include the following steps S201 to S203:

Step S201: Determine current idle computing power resource informationof an edge computing node according to maximum computing power resourceinformation corresponding to a current operating frequency and occupiedcomputing power resource information, and determine an operating stateof the edge computing node according to the current idle computing powerresource information.

The current idle computing power resource information is computing powerresources of the edge computing node unoccupied by the targetapplication during operation of the target application, i.e., unusedcomputing power resource information of the edge computing node.

Specifically, the occupied computing power resource information may besubtracted from the maximum computing power resource informationcorresponding to the current operating frequency (e.g., the occupiedcomputing power resource information is subtracted from the maximumcomputing power resource information corresponding to the currentoperating frequency), thereby obtaining the current idle computing powerresource information of the edge computing node. The operating state ofthe edge computing node may be determined according to the current idlecomputing power resource information. The specific method includes:matching the current idle computing power resource information with anidle resource threshold; when the current idle computing power resourceinformation is greater than the idle resource threshold, determining theoperating state of the edge computing node as a non-full-load operatingstate; and when the current idle computing power resource information isless than the idle resource threshold, determining the operating stateof the edge computing node as a full-load operating state. In someembodiments, when the current idle computing power resource informationis equal to the idle resource threshold, the operating state of the edgecomputing node may also be determined as the non-full-load operatingstate. The idle resource threshold herein may correspond to thethreshold in the embodiment corresponding to FIG. 4 , and thenon-full-load operating state may be understood as the computing powernon-full-load state. The full-load operating state may be understood asthe computing power full-load state. For the specific determinationmethod, reference may be made to the descriptions for step S41 in theembodiment corresponding to FIG. 4 , and details are not describedherein again.

Step S202: When the operating state of the edge computing node is thefull-load operating state, determine a target operating frequency of theedge computing node within a target time period according to theoccupied computing power resource information and expected demandcomputing power resource information.

Specifically, when the operating state of the edge computing node is thefull-load operating state, the expected demand computing power resourceinformation is compared with the occupied computing power resourceinformation. When the expected demand computing power resourceinformation is greater than the occupied computing power resourceinformation, a mapping table is obtained. The mapping table includes Nmapping relationships. A mapping relationship includes a correspondingrelationship between a configured operating frequency (the configuredoperating frequency may refer to an operating frequency in the mappingtable) and configured maximum computing power resource information (inthe mapping table, one operating frequency corresponds to one piece ofmaximum computing power resource information, the operating frequency inthe mapping table may be referred as the configured operating frequency,and the maximum computing power resource information corresponding toeach configured operating frequency may be referred as configuredmaximum computing power resource information). In the mapping table, aconfigured operating frequency corresponding to the configured maximumcomputing power resource information greater than the expected demandcomputing power resource information may be determined as the targetoperating frequency (in some embodiments, a configured operatingfrequency corresponding to the configured maximum computing powerresource information equal to the expected demand computing powerresource information may also be determined as the target operatingfrequency). Then, the operating frequency of the edge computing node isadjusted from the current operating frequency to the target operatingfrequency. Here, when the configured operating frequency correspondingto the configured maximum computing power resource information greaterthan the expected demand computing power resource information in themapping table is determined as the target operating frequency, theconfigured maximum computing power resource information greater than theexpected demand computing power resource information is slightly greaterthan the expected demand computing power resource information (e.g., thesmallest one of the configured maximum computing power resourceinformation greater than the expected demand computing power resourceinformation in the mapping table), so that the computing power resourcesprovided within the target time period can be prevented from beingexcessively large (i.e., the edge computing node is prevented fromoperating at an excessively large operating frequency within the targettime period).

In some embodiments, it may be understood that, after the expecteddemand computing power resource information is compared with theoccupied computing power resource information, when the expected demandcomputing power resource information is less than the occupied computingpower resource information, the current operating frequency isdetermined as the target operating frequency. In some embodiments, afterthe expected demand computing power resource information is comparedwith the occupied computing power resource information, when theexpected demand computing power resource information is equal to theoccupied computing power resource information, the current operatingfrequency is determined as the target operating frequency.

Step S203: When the operating state of the edge computing node is thenon-full-load operating state and the expected demand computing powerresource information is greater than the occupied computing powerresource information, determine a target operating frequency of the edgecomputing node within the target time period according to the currentidle computing power resource information, the occupied computing powerresource information, and the expected demand computing power resourceinformation.

Specifically, when the operating state of the edge computing node is thenon-full-load operating state and the expected demand computing powerresource information is greater than the occupied computing powerresource information, an absolute value of a resource difference betweenthe expected demand computing power resource information and theoccupied computing power resource information is determined. When thecurrent idle computing power resource information is less than theabsolute value of the resource difference, a mapping table is obtained.The mapping table includes N mapping relationships. A mappingrelationship includes a corresponding relationship between a configuredoperating frequency and configured maximum computing power resourceinformation. In the mapping table, a configured operating frequencycorresponding to the configured maximum computing power resourceinformation greater than (or equal to) the expected demand computingpower resource information may be determined as the target operatingfrequency. The operating frequency of the edge computing node isadjusted from the current operating frequency to the target operatingfrequency. Here, when the configured operating frequency correspondingto the configured maximum computing power resource information greaterthan the expected demand computing power resource information in themapping table is determined as the target operating frequency, theconfigured maximum computing power resource information greater than theexpected demand computing power resource information is slightly greaterthan the expected demand computing power resource information (e.g., thesmallest one of the configured maximum computing power resourceinformation greater than the expected demand computing power resourceinformation in the mapping table), so that the computing power resourcesprovided within the target time period can be prevented from beingexcessively large.

In some embodiments, it may be understood that, after the absolute valueof the resource difference between the expected demand computing powerresource information and the occupied computing power resourceinformation is determined, when the current idle computing powerresource information is greater than or equal to the absolute value ofthe resource difference, the current operating frequency is determinedas the target operating frequency.

In some embodiments, it may be understood that, when the operating stateof the edge computing node is the non-full-load operating state and theexpected demand computing power resource information is less than theoccupied computing power resource information, a mapping table isobtained. The mapping table includes N mapping relationships. A mappingrelationship includes a corresponding relationship between a configuredoperating frequency, configured maximum computing power resourceinformation, and configured operating consumption (generally, the edgecomputing node, when operating at a certain frequency, generates powerconsumption (power loss (Watts)), different operating frequencies maygenerate different power consumption, the operating consumption hereinmay refer to power consumption, and the operating consumptioncorresponding to each configured operating frequency in the mappingtable may be referred to as configured operating consumption). Theconfigured operating consumption corresponding to the current operatingfrequency in the mapping table is determined as to-be-comparedconfigured operating consumption, and a target operating frequency ofthe edge computing node within the target time period is determinedaccording to the to-be-compared configured operating consumption.

The specific method for determining a target operating frequency of theedge computing node within the target time period according to theto-be-compared configured operating consumption includes: determining,in the mapping table, configured operating consumption corresponding tothe configured maximum computing power resource information greater thanor equal to the expected demand computing power resource information astarget operating consumption; comparing the target operating consumptionwith the to-be-compared configured operating consumption; when thetarget operating consumption is less than the to-be-compared configuredoperating consumption, determining a configured operating frequencycorresponding to the target operating consumption as the targetoperating frequency, and adjusting the operating frequency of the edgecomputing node from the current operating frequency to the targetoperating frequency; and when the target operating consumption isgreater than or equal to the configured operating consumption,determining the current operating frequency as the target operatingfrequency. Here, when the configured operating frequency correspondingto the configured maximum computing power resource information greaterthan the expected demand computing power resource information in themapping table is determined as the target operating frequency, theconfigured maximum computing power resource information greater than theexpected demand computing power resource information is slightly greaterthan the expected demand computing power resource information (e.g., thesmallest one of the configured maximum computing power resourceinformation greater than the expected demand computing power resourceinformation in the mapping table), so that the computing power resourcesprovided within the target time period can be prevented from beingexcessively large (i.e., the operating frequency of the edge computingnode within the target time period is prevented from being excessivelylarge).

In some embodiments, when the operating state of the edge computing nodeis the non-full-load operating state and the expected demand computingpower resource information is equal to the occupied computing powerresource information, the current operating frequency is maintainedunchanged (i.e., the current operating frequency is taken as the targetoperating frequency within the target time period).

The operating consumption may be understood as the power consumption ofthe edge computing node, and the mapping table may be understood as arelationship mapping table. For specific implementation of steps S201 toS203, reference may be made to the descriptions for steps S41 to S49 inthe embodiment corresponding to FIG. 4 , and details are not describedherein again.

Further, FIG. 6 is a diagram of a system architecture provided accordingto an embodiment of this application. As shown in FIG. 6 , the systemmay include a management server and an edge computing node, and the edgecomputing node may include a computing power information collectionmodule, a frequency control module, a computing power guarantee module,and a power consumption information collection module. For ease ofunderstanding, functions corresponding to the modules are describedbelow.

The computing power information collection module: may mainly includeCPU computing power information collection, GPU computing powerinformation collection, and local unit information collection. The CPUcomputing power information collection mainly collects the CPU model,the current operating frequency, the maximum computing power that can bereached currently, the computing power currently actually used (i.e.,the occupied computing power), the CPU power consumption, etc. The GPUcomputing power information collection mainly collects the GPU model,the current operating frequency, the maximum computing power that can bereached currently, the computing power currently actually used, and theGPU power consumption. The local unit information collection mainlycollects the local unit model of the edge computing node and the currentoverall power consumption of the local unit.

The frequency control module: is mainly used for CPU frequency controland GPU frequency control. The CPU frequency control is mainly: toincrease or reduce the operating frequency of the CPU according torequirements of the computing power guarantee module. The GPU frequencycontrol is mainly: to increase or reduce the operating frequency of theGPU according to requirements of the computing power guarantee module.

The computing power guarantee module delivers CPU and GPU frequencyadjustment instructions to the frequency control module according to aninstruction of the management server and an actual computing power ofthe local unit. The computing power guarantee module is mainly used forfrequency adjustment and basic computing power guarantee. The frequencyadjustment is mainly: to increase or reduce the operating frequency ofthe CPU and GPU according to an instruction of the management server.When the operating frequency is needed to be increased, the adjustmentmay be directly performed. When the operating frequency is needed to bereduced, a change condition of the current CPU and GPU power consumptionmay be determined, and if the power consumption after frequencyreduction is not reduced, the frequency reduction operation is notperformed, and the corresponding information is notified to themanagement server. The basic computing power guarantee is mainly: toregularly check the current CPU and GPU load conditions, and if thecurrent CPU and GPU have reached a full-load condition, the operatingfrequency is increased in real time.

The power consumption information collection module: is mainly used forCPU power consumption collection (collecting real-time power consumptionof the CPU), GPU power consumption collection (collecting real-timepower consumption of the GPU), overall unit power consumption collection(collecting real-time power consumption of the overall unit), andcomputing power data. After collection, the data is summarized andreported to the management server.

The management server may include a data analysis module, a computingpower prediction module, and a frequency adjustment strategy module. Forease of understanding, functions corresponding to the modules aredescribed below.

The data analysis module: is configured to receive a data report of theedge computing node, and perform preliminary analysis for use by othermodules.

The computing power prediction module: is configured to predict a changecondition of the next-stage computing power according to a newly entereduser and a historical trend, i.e., predicting expected demand computingpower resource information of the next stage.

The frequency adjustment strategy module: is configured to determine,according to a current computing power usage condition and the operatingfrequency of the CPU and the GPU, whether to increase or reduce theoperating frequency of the CPU and the GPU.

Further, FIG. 7 is a schematic structural diagram of a data processingapparatus based on edge computing provided according to an embodiment ofthis application. The data processing apparatus based on edge computingmay be a computer-readable instruction (including program code)operating in a computer device, for example, the data processingapparatus based on edge computing is application software. The dataprocessing apparatus based on edge computing may be configured toexecute the method shown in FIG. 3 . As shown in FIG. 7 , the dataprocessing apparatus 1 based on edge computing includes: a usedcomputing power acquisition module 11, an object prediction module 12,an expected computing power determination module 13, and a frequencydetermination module 14.

The used computing power acquisition module 11 is configured to obtainoccupied computing power resource information of a target applicationfor an edge computing node. The occupied computing power resourceinformation is computing power resource information of the edgecomputing node occupied during operation of the target application.

The object prediction module 12 is configured to predict a newly addedservice object, an offline service object, and a scenario switchingservice object for the target application within a target time period.

The expected computing power determination module 13 is configured todetermine expected demand computing power resource information of thetarget application for the edge computing node within the target timeperiod according to the newly added service object, the offline serviceobject, and the scenario switching service object.

The frequency determination module 14 is configured to obtain a currentoperating frequency of the edge computing node.

The frequency determination module 14 is further configured to determinea target operating frequency of the edge computing node within thetarget time period according to maximum computing power resourceinformation corresponding to the current operating frequency, theoccupied computing power resource information, and the expected demandcomputing power resource information. The maximum computing powerresource information corresponding to the target operating frequencysatisfies the expected demand computing power resource information.

For the specific implementation of the used computing power acquisitionmodule 11, the object prediction module 12, the expected computing powerdetermination module 13, and the frequency determination module 14,reference may be made to the descriptions for step S101-S103 in theembodiment corresponding to FIG. 3 , and details are not describedherein again.

In an embodiment, the frequency determination module 14 includes: anidle computing power determination unit 141, an operating statedetermination unit 142, and a target frequency determination unit 143.

The idle computing power determination unit 141 is configured todetermine current idle computing power resource information of the edgecomputing node according to the maximum computing power resourceinformation corresponding to the current operating frequency and theoccupied computing power resource information.

The operating state determination unit 142 is configured to determine anoperating state of the edge computing node according to the current idlecomputing power resource information.

The target frequency determination unit 143 is configured to, when theoperating state of the edge computing node is a full-load operatingstate, determine a target operating frequency of the edge computing nodewithin the target time period according to the occupied computing powerresource information and the expected demand computing power resourceinformation.

The target frequency determination unit 143 is further configured to,when the operating state of the edge computing node is a non-full-loadoperating state and the expected demand computing power resourceinformation is greater than the occupied computing power resourceinformation, determine a target operating frequency of the edgecomputing node within the target time period according to the currentidle computing power resource information, the occupied computing powerresource information, and the expected demand computing power resourceinformation.

For the specific implementation of the idle computing powerdetermination unit 141, the operating state determination unit 142, andthe target frequency determination unit 143, reference may be made tothe descriptions for step S103 in the embodiment corresponding to FIG. 3, and details are not described herein again.

In an embodiment, the operating state determination unit 142 includes: amatching subunit 1421 and a state determination subunit 1422.

The matching subunit 1421 is configured to match the current idlecomputing power resource information with an idle resource threshold.

The state determination subunit 1422 is configured to, when the currentidle computing power resource information is greater than the idleresource threshold, determine the operating state of the edge computingnode as a non-full-load operating state.

The state determination subunit 1422 is further configured to, when thecurrent idle computing power resource information is less than the idleresource threshold, determine the operating state of the edge computingnode as a full-load operating state.

For the specific implementation of the matching subunit 1421 and thestate determination subunit 1422, reference may be made to thedescriptions for step S103 in the embodiment corresponding to FIG. 3 ,and details are not described herein again.

In an embodiment, the target frequency determination unit 143 includes:a computing power comparison subunit 1431, a frequency acquisitionsubunit 1432, and a first frequency adjustment subunit 1433.

The computing power comparison subunit 1431 is configured to, when theoperating state of the edge computing node is the full-load operatingstate, compare the expected demand computing power resource informationwith the occupied computing power resource information.

The frequency acquisition subunit 1432 is configured to, when theexpected demand computing power resource information is greater than theoccupied computing power resource information, obtain a mapping table.The mapping table includes N mapping relationships. A mappingrelationship includes a corresponding relationship between a configuredoperating frequency and configured maximum computing power resourceinformation.

The frequency acquisition subunit 1432 is further configured to,determine, in the mapping table, a configured operating frequencycorresponding to the configured maximum computing power resourceinformation greater than the expected demand computing power resourceinformation as the target operating frequency.

The first frequency adjustment subunit 1433 is configured to adjust theoperating frequency of the edge computing node from the currentoperating frequency to the target operating frequency.

For the specific implementation of the computing power comparisonsubunit 1431, the frequency acquisition subunit 1432, and the firstfrequency adjustment subunit 1433, reference may be made to thedescriptions for step S103 in the embodiment corresponding to FIG. 3 ,and details are not described herein again.

In an embodiment, the target frequency determination unit 143 furtherincludes: a first frequency determination subunit 1434.

The first frequency determination subunit 1434 is configured to, whenthe expected demand computing power resource information is less thanthe occupied computing power resource information, determine the currentoperating frequency as the target operating frequency.

In an embodiment, the target frequency determination unit 143 includes:a difference determination subunit 1435, a difference comparison subunit1436, and a second frequency adjustment subunit 1437.

The difference determination subunit 1435 is configured to, when theoperating state of the edge computing node is the non-full-loadoperating state and the expected demand computing power resourceinformation is greater than the occupied computing power resourceinformation, determine an absolute value of a resource differencebetween the expected demand computing power resource information and theoccupied computing power resource information.

The difference comparison subunit 1436 is configured to, when thecurrent idle computing power resource information is less than theabsolute value of the resource difference, obtain a mapping table. Themapping table includes N mapping relationships. A mapping relationshipincludes a corresponding relationship between a configured operatingfrequency and configured maximum computing power resource information.

The difference comparison subunit 1436 is further configured to,determine, in the mapping table, a configured operating frequencycorresponding to the configured maximum computing power resourceinformation greater than the expected demand computing power resourceinformation as the target operating frequency.

The second frequency adjustment subunit 1437 is configured to adjust theoperating frequency of the edge computing node from the currentoperating frequency to the target operating frequency.

For the specific implementation of the difference determination subunit1435, the difference comparison subunit 1436, and the second frequencyadjustment subunit 1437, reference may be made to the descriptions forstep S103 in the embodiment corresponding to FIG. 3 , and details arenot described herein again.

In an embodiment, the target frequency determination unit 143 furtherincludes: a second frequency determination subunit 1438.

The second frequency determination subunit 1438 is further configuredto, when the current idle computing power resource information isgreater than the absolute value of the resource difference, determinethe current operating frequency as the target operating frequency.

In an embodiment, the frequency determination module 14 furtherincludes: a table acquisition unit 144 and a consumption acquisitionunit 145.

The table acquisition unit 144 is configured to, when the operatingstate of the edge computing node is the non-full-load operating stateand the expected demand computing power resource information is lessthan the occupied computing power resource information, obtain a mappingtable. The mapping table includes N mapping relationships. A mappingrelationship includes a corresponding relationship between a configuredoperating frequency, configured maximum computing power resourceinformation, and configured operating consumption.

The consumption acquisition unit 145 is configured to determineconfigured operating consumption corresponding to the current operatingfrequency in the mapping table as to-be-compared configured operatingconsumption.

The consumption acquisition unit 145 is further configured to determinea target operating frequency of the edge computing node within thetarget time period according to the to-be-compared configured operatingconsumption.

For the specific implementation of the table acquisition unit 144 andthe consumption acquisition unit 145, reference may be made to thedescriptions for step S103 in the embodiment corresponding to FIG. 3 ,and details are not described herein again.

In an embodiment, the consumption acquisition unit 145 is furtherspecifically configured to, determine, in the mapping table, configuredoperating consumption corresponding to the configured maximum computingpower resource information greater than the expected demand computingpower resource information as target operating consumption.

The consumption acquisition unit 145 is further specifically configuredto compare the target operating consumption with the to-be-comparedconfigured operating consumption.

The consumption acquisition unit 145 is further specifically configuredto, when the target operating consumption is less than theto-be-compared configured operating consumption, determine a configuredoperating frequency corresponding to the target operating consumption asthe target operating frequency, and adjust the operating frequency ofthe edge computing node from the current operating frequency to thetarget operating frequency.

The consumption acquisition unit 145 is further specifically configuredto, when the target operating consumption is greater than the configuredoperating consumption, determine the current operating frequency as thetarget operating frequency.

In an embodiment, the used computing power acquisition module 11includes: an operating information acquisition unit 111 and a usedresource statistics collection unit 112.

The operating information acquisition unit 111 is configured to obtain Npieces of current operating information of the target application. Onepiece of current operating information includes an application activityscenario where an online service object is located, and service objectoccupied resource information in the application activity scenario.

The used resource statistics collection unit 112 is configured to obtainservice object occupied resource information corresponding to each pieceof current operating information, to obtain N pieces of service objectoccupied resource information.

The used resource statistics collection unit 112 is further configuredto determine a sum of the N pieces of service object occupied resourceinformation as the occupied computing power resource information of thetarget application for the edge computing node.

For the specific implementation of the operating information acquisitionunit 111 and the used resource statistics collection unit 112, referencemay be made to the descriptions for step S101 in the embodimentcorresponding to FIG. 3 , and details are not described herein again.

In an embodiment, the N pieces of current operating information includeone or more application activity scenarios. The one or more applicationactivity scenarios include an application activity scenario M_(i). i isa positive integer.

The expected computing power determination module 13 includes: an objectcomputing power statistics collection unit 131, an average computingpower determination unit 132, and an expected computing powerdetermination unit 133.

The object computing power statistics collection unit 131 is configuredto determine, from N online service objects, an online service object,of which an application activity scenario is an application activityscenario M_(i), as a service object to be subjected to statisticscollection.

The object computing power statistics collection unit 131 is furtherconfigured to collect statistics about total service object occupiedresource information of the service object to be subjected to statisticscollection in the application activity scenario M_(i).

The average computing power determination unit 132 is configured toobtain an object quantity corresponding to the service object to besubjected to statistics collection, and determine, according the totalservice object occupied resource information and the object quantity,average computing power demand information corresponding to theapplication activity scenario M_(i).

The expected computing power determination unit 133 is configured to, inresponse to determining average computing power demand informationrespectively corresponding to the one or more application activityscenarios, determine expected demand computing power resourceinformation of the target application for the edge computing node withinthe target time period according to the average computing power demandinformation respectively corresponding to the one or more applicationactivity scenarios, the newly added service object, the offline serviceobject, and the scenario switching service object.

For the specific implementation of the object computing power statisticscollection unit 131, the average computing power determination unit 132,and the expected computing power determination unit 133, reference maybe made to the descriptions for step S102 in the embodimentcorresponding to FIG. 3 , and details are not described herein again.

In an embodiment, the expected computing power determination unit 133 isfurther specifically configured to obtain a newly added quantitycorresponding to the newly added service object, an offline quantitycorresponding to the offline service object, and a switching quantitycorresponding to the scenario switching object.

The expected computing power determination unit 133 is furtherspecifically configured to predict an initial login scenariocorresponding to the newly added service object. The one or moreapplication activity scenarios include the initial login scenario.

The expected computing power determination unit 133 is furtherspecifically configured to determine an application activity scenariowhere the offline service object having an offline behavior is locatedas an offline application activity scenario.

The expected computing power determination unit 133 is furtherspecifically configured to determine an application activity scenariowhere the scenario switching object is located before scenario switchingas an initial application activity scenario, and an application activityscenario where the scenario switching object is located after scenarioswitching as a target application activity scenario.

The expected computing power determination unit 133 is furtherspecifically configured to determine expected demand computing powerresource information of the target application for the edge computingnode within the target time period according to the average computingpower demand information respectively corresponding to the initial loginscenario, the offline application activity scenario, the initialapplication activity scenario, and the target application activityscenario, as well as the switching quantity, the offline quantity, andthe newly added quantity.

In an embodiment, the expected computing power determination unit 133 isfurther specifically configured to determine first variable computingpower resource information corresponding to the newly added serviceobject according to the newly added quantity and the average computingpower demand information corresponding to the initial login scenario.

The expected computing power determination unit 133 is furtherspecifically configured to determine second variable computing powerresource information corresponding to the offline service objectaccording to the offline quantity and the average computing power demandinformation corresponding to the online application activity scenario.

The expected computing power determination unit 133 is furtherspecifically configured to determine third variable computing powerresource information corresponding to the scenario switching objectaccording to the average computing power demand informationcorresponding to the initial application activity scenario, the averagecomputing power demand information corresponding to the targetapplication activity scenario, and the switching quantity.

The expected computing power determination unit 133 is furtherspecifically configured to determine expected demand computing powerresource information of the target application for the edge computingnode within the target time period according to the first variablecomputing power resource information, the second variable computingpower resource information, the third variable computing power resourceinformation, and the occupied computing power resource information ofthe edge computing node.

In the embodiments of this application, the main factors affecting thecomputing power resource change of the edge computing node include thefactors that the object is online, gets offline, and gets online.Therefore, in this application, a newly added service object, an offlineservice object, and a scenario switching service object for the targetapplication within a target time period may be predicted, and expecteddemand computing power resource information of the target applicationfor the edge computing node within the target time period may bedetermined according to the newly added service object, the offlineservice object, and the scenario switching service object. Therefore,whether the current operating frequency is increased, reduced, ormaintained unchanged within the target time period may be determinedaccording to the expected demand computing power resource information ofthe target time period, the occupied computing power resourceinformation of the edge computing node, and the maximum computing powerresource information corresponding to the current operating frequency ofthe edge computing node (i.e., determining the target operatingfrequency within the target time period). The maximum computing powerresource information of the edge computing node corresponds to theoperating frequency of the edge computing node (e.g., the greater theoperating frequency, the greater the maximum computing power resourceinformation), and the operating frequency and the operating consumption(i.e., power consumption) of the edge computing node also have acorresponding relationship. Therefore, by adjusting the operatingfrequency of the edge computing node in a timely fashion (increased,reduced or maintained unchanged), the maximum computing power resourceinformation corresponding to the target operating frequency within thetarget time period can satisfy the expected demand computing powerresource information, so that the problem that the computing powerresources provided by the edge computing node are not matched withactually required computing power resources can be reduced (e.g., theproblem that the computing power resources provided by the edgecomputing node are excessively large but the actually required computingpower resources are small is reduced), the computing power resourcesprovided by the edge computing node are matched with the requiredcomputing power resources within the target time period, the operatingconsumption of the edge computing node cannot be excessively large, andthe operation costs are reduced. That is, in this application, byadjusting the operating frequency of the edge computing node, thecomputing power resources provided by the edge computing node will notbe much greater than the expected demand computing power resourceinformation (i.e., reducing the operation costs), and the computingpower resources will not be much less than the expected demand computingpower resource information so that the demand cannot be satisfied,thereby dynamically balancing the computing power demand and theoperation costs. That is, in this application, the operation costs ofthe edge computing node can be reduced while the computing power demandis satisfied.

Further, FIG. 8 is a schematic structural diagram of a computer deviceprovided according to an embodiment of this application. As shown inFIG. 8 , the apparatus 1 in the embodiment corresponding to FIG. 7 maybe applied to the computer device 1000, and the computer device 1000includes: a processor 1001, a network interface 1004, and a memory 1005.In addition, the computer device 1000 further includes: an objectinterface 1003, and at least one communication bus 1002. Thecommunication bus 1002 is configured to implement connection andcommunication between these components. The object interface 1003includes a display and a keyboard. In some embodiments, the objectinterface 1003 further includes a standard wired interface and wirelessinterface. The network interface 1004 includes a standard wiredinterface and wireless interface (e.g., a WiFi interface). The memory1005 may be a high-speed RAM memory, or may be a non-transitorycomputer=readable memory, for example, at least one magnetic diskmemory. In some embodiments, the memory 1005 may further be at least onestorage apparatus that is located far away from the processor 1001. Asshown in FIG. 8 , the memory 1005 as a computer-readable storage mediumincludes an operating system, a network communication module, an objectinterface module, and a device control application program.

In the computer device 1000 shown in FIG. 8 , the network interface 1004may provide a network communication function. The object interface 1003is mainly configured to provide an input interface for an object. Theprocessor 1001 may be configured to invoke the device controlapplication program stored in the memory 1005, to perform the followingoperations: obtaining occupied computing power resource information of atarget application for an edge computing node, the occupied computingpower resource information being computing power resource information ofthe edge computing node occupied during operation of the targetapplication; predicting a newly added service object, an offline serviceobject, and a scenario switching service object for the targetapplication within a target time period, and determining expected demandcomputing power resource information of the target application for theedge computing node within the target time period according to the newlyadded service object, the offline service object, and the scenarioswitching service object; and obtaining a current operating frequency ofthe edge computing node, and determining a target operating frequency ofthe edge computing node within the target time period according tomaximum computing power resource information corresponding to thecurrent operating frequency, the occupied computing power resourceinformation, and the expected demand computing power resourceinformation, the maximum computing power resource informationcorresponding to the target operating frequency satisfying the expecteddemand computing power resource information.

It is to be understood that, the computer device 1000 described in theembodiments of this application may perform the descriptions for thedata processing method based on edge computing in the embodimentscorresponding to FIGS. 3 to 5 , and may also perform the descriptionsfor the data processing apparatus 1 based on edge computing in theembodiment corresponding to FIG. 7 , and details are not describedherein again. In addition, the descriptions of beneficial effects of thesame method are not described herein again.

The embodiments of this application further provide a non-transitorycomputer-readable storage medium. The computer-readable storage mediumstores computer-readable instructions which are performed by thecomputer device 1000 for data processing mentioned above. When theprocessor executes the computer-readable instructions, the processor canperform the descriptions for the data processing method in theembodiments corresponding to FIGS. 3 to 5 . Therefore, details are notdescribed herein again. In addition, the descriptions of beneficialeffects of the same method are not described herein again. For technicaldetails that are not disclosed in the computer-readable storage mediumembodiments of this application, please refer to the descriptions of themethod embodiments of this application.

The computer-readable storage medium may be a data processing apparatusbased on edge computing or an internal storage unit of the computerdevice provided according to any embodiment above, such as a hard diskor an internal memory of the computer device. The computer-readablestorage medium may also be an external storage device of the computerdevice, such as an insertion-type hard disk drive, a smart media card(SMC), a secure digital (SD) card, and a flash card, which are providedon the computer device. Further, the computer-readable storage mediummay further include both an internal storage unit and an externalstorage device of the computer device. The computer-readable storagemedium is configured to store the computer-readable instructions andother programs and data required by the computer device. Thecomputer-readable storage medium can further be configured totemporarily store outputted data or data to be outputted.

According to one aspect of this application, a computer program productor a computer program is provided. The computer program product orcomputer program includes computer-readable instructions, thecomputer-readable instructions being stored in the computer-readablestorage medium. A processor of the computer device reads thecomputer-readable instructions from the computer-readable storagemedium, and the processor executes the computer-readable instructions tocause the computer device to perform the method provided according toone aspect of the embodiments of this application.

The terms such as “first” and “second” in the description, claims, andthe accompanying drawings of this application are used to distinguishdifferent objects and are not used to describe a specific sequence. Inaddition, the term “include” and any variant thereof are intended tocover a non-exclusive inclusion. For example, a process, method,apparatus, product, or device that includes a series of steps or unitsis not limited to the listed steps or units; and instead, furtherincludes a step or unit that is not listed, or further includes anotherstep or unit that is intrinsic to the process, method, apparatus,product, or device. In this application, the term “module” or “unit” inthis application refers to a computer program or part of the computerprogram that has a predefined function and works together with otherrelated parts to achieve a predefined goal and may be all or partiallyimplemented by using software, hardware (e.g., processing circuitryand/or memory configured to perform the predefined functions), or acombination thereof. Each module or unit can be implemented using one ormore processors (or processors and memory). Likewise, a processor (orprocessors and memory) can be used to implement one or more modules orunits.

A person skilled in the art may understand that, units and algorithmsteps of the examples described in the foregoing disclosed embodimentsmay be implemented by electronic hardware, computer software, or acombination thereof. To clearly describe the interchangeability betweenthe hardware and the software, the foregoing has generally describedcompositions and steps of each example based on functions. Whether thefunctions are executed in a mode of hardware or software depends onparticular applications and design constraint conditions of thetechnical solutions. A person skilled in the art may use differentmethods to implement the described functions for each particularapplication, but it is not be considered that the implementation goesbeyond the scope of this application.

The method and the related apparatus provided in the embodiments of thisapplication are described with reference to the flowchart and/orschematic structural diagram of the method provided in the embodimentsof this application, and specifically, each process and/or block of theflowchart and/or schematic structural diagram of the method and/or acombination of the processes and/or blocks in the flowchart and/or theblock diagram can be implemented by the computer program instructions.These computer program instructions may be provided to a general-purposecomputer, a dedicated computer, an embedded processor, or a processor ofanother programmable data processing device to generate a machine, sothat the instructions executed by the computer or the processor of theanother programmable data processing device generate an apparatus forimplementing a specific function in one or more processes in theflowcharts and/or in one or more blocks in the schematic structuraldiagrams. These computer program instructions may also be stored in acomputer-readable memory that can instruct a computer or any otherprogrammable data processing device to work in a specific manner, sothat the instructions stored in the computer-readable memory generate anartifact that includes an instruction apparatus. The instructionapparatus implements a specific function in one or more processes in theflowcharts and/or in one or more blocks in the schematic structuraldiagrams. These computer program instructions may also be loaded onto acomputer or another programmable data processing device, so that aseries of operations and steps are performed on the computer or theanother programmable device, thereby generating computer-implementedprocessing. Therefore, the instructions executed on the computer or theanother programmable device provide steps for implementing a specificfunction in one or more processes in the flowcharts and/or in one ormore blocks in the schematic structural diagrams.

What is disclosed above is merely exemplary embodiments of thisapplication, and certainly is not intended to limit the scope of theclaims of this application. Therefore, equivalent variations made inaccordance with the claims of this application shall fall within thescope of this application.

What is claimed is:
 1. A method for allocating edge computing resourcesperformed by at least one computer device, the method comprising:obtaining computing power resource information occupied by a targetapplication at an edge computing node and a current operating frequencyof the edge computing node during operation of the target application;determining expected demand computing power resource information of thetarget application for the edge computing node within a target timeperiod according to a number of newly added service objects, offlineservice objects, and scenario switching service objects during thetarget time period; and determining a target operating frequency of theedge computing node within the target time period according to thecurrent operating frequency, the occupied computing power resourceinformation, and the expected demand computing power resourceinformation.
 2. The method according to claim 1, wherein the determininga target operating frequency of the edge computing node within thetarget time period according to the current operating frequency, theoccupied computing power resource information, and the expected demandcomputing power resource information comprises: determining an operatingstate of the edge computing node according to the current operatingfrequency and the occupied computing power resource information; whenthe operating state of the edge computing node is a full-load operatingstate, determining a target operating frequency of the edge computingnode within the target time period according to the occupied computingpower resource information and the expected demand computing powerresource information; and when the operating state of the edge computingnode is a non-full-load operating state and the expected demandcomputing power resource information is greater than the occupiedcomputing power resource information, determining a target operatingfrequency of the edge computing node within the target time periodaccording to the current idle computing power resource information, theoccupied computing power resource information, and the expected demandcomputing power resource information.
 3. The method according to claim2, wherein the determining an operating state of the edge computing nodeaccording to the current operating frequency and the occupied computingpower resource information comprises: determining current idle computingpower resource information of the edge computing node according to thecurrent operating frequency and the occupied computing power resourceinformation; matching the current idle computing power resourceinformation with an idle resource threshold; when the current idlecomputing power resource information is greater than the idle resourcethreshold, determining the operating state of the edge computing node asthe non-full-load operating state; and when the current idle computingpower resource information is less than the idle resource threshold,determining the operating state of the edge computing node as thefull-load operating state.
 4. The method according to claim 2, whereinthe determining a target operating frequency of the edge computing nodewithin the target time period according to the occupied computing powerresource information and the expected demand computing power resourceinformation comprises: when the expected demand computing power resourceinformation is greater than the occupied computing power resourceinformation, obtaining a mapping table including N mappingrelationships, each mapping relationship comprising a correspondingrelationship between a configured operating frequency and configuredmaximum computing power resource information; determining, in themapping table, a configured operating frequency corresponding to aconfigured maximum computing power resource information greater than theexpected demand computing power resource information as the targetoperating frequency; and adjusting the operating frequency of the edgecomputing node from the current operating frequency to the targetoperating frequency.
 5. The method according to claim 4, wherein themethod further comprises: when the expected demand computing powerresource information is less than the occupied computing power resourceinformation, determining the current operating frequency as the targetoperating frequency.
 6. The method according to claim 2, wherein thedetermining a target operating frequency of the edge computing nodewithin the target time period according to the current idle computingpower resource information, the occupied computing power resourceinformation, and the expected demand computing power resourceinformation comprises: when the expected demand computing power resourceinformation is greater than the occupied computing power resourceinformation, determining an absolute value of a resource differencebetween the expected demand computing power resource information and theoccupied computing power resource information; when the current idlecomputing power resource information is less than the absolute value ofthe resource difference, obtaining a mapping table including N mappingrelationships, each mapping relationship comprising a correspondingrelationship between a configured operating frequency and configuredmaximum computing power resource information; determining, in themapping table, a configured operating frequency corresponding to theconfigured maximum computing power resource information greater than theexpected demand computing power resource information as the targetoperating frequency; and adjusting the operating frequency of the edgecomputing node from the current operating frequency to the targetoperating frequency.
 7. The method according to claim 6, wherein themethod further comprises: when the current idle computing power resourceinformation is greater than the absolute value of the resourcedifference, determining the current operating frequency as the targetoperating frequency.
 8. The method according to claim 2, wherein themethod further comprises: when the operating state of the edge computingnode is a non-full-load operating state and the expected demandcomputing power resource information is less than the occupied computingpower resource information, obtaining a mapping table including Nmapping relationships, each mapping relationship comprising acorresponding relationship between a configured operating frequency,configured maximum computing power resource information, and configuredoperating consumption; determining configured operating consumptioncorresponding to the current operating frequency in the mapping table asto-be-compared configured operating consumption; and determining atarget operating frequency of the edge computing node within the targettime period according to the to-be-compared configured operatingconsumption.
 9. The method according to claim 8, wherein the determininga target operating frequency of the edge computing node within thetarget time period according to the to-be-compared configured operatingconsumption comprises: determining, in the mapping table, configuredoperating consumption corresponding to configured maximum computingpower resource information greater than the expected demand computingpower resource information as target operating consumption; andcomparing the target operating consumption with the to-be-comparedconfigured operating consumption; and when the target operatingconsumption is less than the to-be-compared configured operatingconsumption, determining a configured operating frequency correspondingto the target operating consumption as the target operating frequency,and adjusting the operating frequency of the edge computing node fromthe current operating frequency to the target operating frequency. 10.The method according to claim 9, wherein the method further comprises:when the target operating consumption is greater than the configuredoperating consumption, determining the current operating frequency asthe target operating frequency.
 11. The method according to claim 1,wherein, the obtaining computing power resource information occupied bya target application at an edge computing node and a current operatingfrequency of the edge computing node during operation of the targetapplication comprises: obtaining N pieces of current operatinginformation of the target application, one piece of current operatinginformation comprising an application activity scenario where an onlineservice object is located, and service object occupied resourceinformation in the application activity scenario; obtaining serviceobject occupied resource information corresponding to each piece ofcurrent operating information, to obtain N pieces of service objectoccupied resource information; and determining a sum of the N pieces ofservice object occupied resource information as the occupied computingpower resource information of the target application for the edgecomputing node.
 12. The method according to claim 11, wherein the Npieces of current operating information comprise one or more applicationactivity scenarios; the one or more application activity scenarioscomprise an application activity scenario M_(i); i is a positiveinteger; and the determining expected demand computing power resourceinformation of the target application for the edge computing node withinthe target time period according to the newly added service object, theoffline service object, and the scenario switching service objectcomprises: determining, from N online service objects, an online serviceobject, of which an application activity scenario is an applicationactivity scenario Mi, as a service object to be subjected to statisticscollection; collecting statistics about total service object occupiedresource information of the service object to be subjected to statisticscollection in the application activity scenario Mi; obtaining an objectquantity corresponding to the service object to be subjected tostatistics collection, and determining, according to the total serviceobject occupied resource information and the object quantity, averagecomputing power demand information corresponding to the applicationactivity scenario Mi; and in response to determining average computingpower demand information respectively corresponding to the one or moreapplication activity scenarios, determining expected demand computingpower resource information of the target application for the edgecomputing node within the target time period according to the averagecomputing power demand information respectively corresponding to the oneor more application activity scenarios, the newly added service object,the offline service object, and the scenario switching service object.13. The method according to claim 12, wherein the determining expecteddemand computing power resource information of the target applicationfor the edge computing node within the target time period according tothe average computing power demand information respectivelycorresponding to the one or more application activity scenarios, thenewly added service object, the offline service object, and the scenarioswitching service object comprises: obtaining a newly added quantitycorresponding to the newly added service object, an offline quantitycorresponding to the offline service object, and a switching quantitycorresponding to the scenario switching object; predicting an initiallogin scenario corresponding to the newly added service object; the oneor more application activity scenarios comprising the initial loginscenario; determining an application activity scenario where the offlineservice object having an offline behavior is located as an offlineapplication activity scenario; determining an application activityscenario where the scenario switching object is located before scenarioswitching as an initial application activity scenario, and determiningan application activity scenario where the scenario switching object islocated after scenario switching as a target application activityscenario; and determining expected demand computing power resourceinformation of the target application for the edge computing node withinthe target time period according to the average computing power demandinformation respectively corresponding to the initial login scenario,the offline application activity scenario, the initial applicationactivity scenario, and the target application activity scenario, as wellas the switching quantity, the offline quantity, and the newly addedquantity.
 14. The method according to claim 11, wherein the determiningexpected demand computing power resource information of the targetapplication for the edge computing node within the target time periodaccording to the average computing power demand information respectivelycorresponding to the initial login scenario, the offline applicationactivity scenario, the initial application activity scenario, and thetarget application activity scenario, as well as the switching quantity,the offline quantity, and the newly added quantity comprises:determining first variable computing power resource informationcorresponding to the newly added service object according to the newlyadded quantity and the average computing power demand informationcorresponding to the initial login scenario; determining second variablecomputing power resource information corresponding to the offlineservice object according to the offline quantity and the averagecomputing power demand information corresponding to the onlineapplication activity scenario; determining third variable computingpower resource information corresponding to the scenario switchingobject according to the average computing power demand informationcorresponding to the initial application activity scenario, the averagecomputing power demand information corresponding to the targetapplication activity scenario, and the switching quantity; anddetermining expected demand computing power resource information of thetarget application for the edge computing node within the target timeperiod according to the first variable computing power resourceinformation, the second variable computing power resource information,the third variable computing power resource information, and theoccupied computing power resource information of the edge computingnode.
 15. The method according to claim 14, wherein the determiningexpected demand computing power resource information of the targetapplication for the edge computing node within the target time periodaccording to the first variable computing power resource information,the second variable computing power resource information, the thirdvariable computing power resource information, and the occupiedcomputing power resource information of the edge computing nodecomprises: adding the occupied computing power resource information, thefirst variable computing power resource information, and the thirdvariable computing power resource information to obtain an added result;and subtracting the second variable computing power resource informationfrom the added result to obtain expected demand computing power resourceinformation of the target application for the edge computing node withinthe target time period.
 16. The method according to claim 1, wherein thedetermining expected demand computing power resource information of thetarget application for the edge computing node within a target timeperiod according to a number of newly added service objects, offlineservice objects, and scenario switching service objects during thetarget time period comprises: predicting the number of newly addedservice objects, offline service objects, and scenario switching serviceobjects for the target application within the target time period. 17.The method according to claim 16, wherein the predicting the number ofnewly added service objects, offline service objects, and scenarioswitching service objects for the target application within the targettime period comprises: obtaining historical service behavior data of thetarget application, and obtaining operational activity information ofthe target application within the target time period; determining anumber of newly added service objects of the edge computing node for thetarget application within the target time period according to theoperational activity information within the target time period and thehistorical service behavior data; and determining a number of offlineservice objects and scenario switching service objects of the edgecomputing node for the target application within the target time periodaccording to the application activity scenario where the service objectis located, the operational activity information within the target timeperiod, and the historical service behavior data.
 18. A computer device,comprising: a processor, a memory, and a network interface, theprocessor being connected to the memory and the network interface, thenetwork interface being configured to provide a network communicationfunction, the memory being configured to store computer-readableinstructions, and the computer-readable instructions, when executed bythe processor, causing the computer device to perform a method forallocating edge computing resources performed by a computer device, themethod comprising: obtaining computing power resource informationoccupied by a target application at an edge computing node and a currentoperating frequency of the edge computing node during operation of thetarget application; determining expected demand computing power resourceinformation of the target application for the edge computing node withina target time period according to a number of newly added serviceobjects, offline service objects, and scenario switching service objectsduring the target time period; and determining a target operatingfrequency of the edge computing node within the target time periodaccording to the current operating frequency, the occupied computingpower resource information, and the expected demand computing powerresource information.
 19. The computer device according to claim 18,wherein the determining a target operating frequency of the edgecomputing node within the target time period according to the currentoperating frequency, the occupied computing power resource information,and the expected demand computing power resource information comprises:determining an operating state of the edge computing node according tothe current operating frequency and the occupied computing powerresource information; when the operating state of the edge computingnode is a full-load operating state, determining a target operatingfrequency of the edge computing node within the target time periodaccording to the occupied computing power resource information and theexpected demand computing power resource information; and when theoperating state of the edge computing node is a non-full-load operatingstate and the expected demand computing power resource information isgreater than the occupied computing power resource information,determining a target operating frequency of the edge computing nodewithin the target time period according to the current idle computingpower resource information, the occupied computing power resourceinformation, and the expected demand computing power resourceinformation.
 20. A non-transitory computer-readable storage medium,storing computer-readable instructions, and the computer-readableinstructions, when executed by a processor of a computer device, causingthe computer device to perform a method for allocating edge computingresources performed by a computer device, the method comprising:obtaining computing power resource information occupied by a targetapplication at an edge computing node and a current operating frequencyof the edge computing node during operation of the target application;determining expected demand computing power resource information of thetarget application for the edge computing node within a target timeperiod according to a number of newly added service objects, offlineservice objects, and scenario switching service objects during thetarget time period; and determining a target operating frequency of theedge computing node within the target time period according to thecurrent operating frequency, the occupied computing power resourceinformation, and the expected demand computing power resourceinformation.